VeLeRo: An inflected verbal lexicon of Standard Romanian and a quantitative analysis of morphological predictability

VeLeRo: An inflected verbal lexicon of Standard Romanian and a quantitative analysis of morphological predictability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report VeLeRo: An inflected verbal lexicon of Standard Romanian and a quantitative analysis of morphological predictability Borja Herce, Bogdan Pricop This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3167337/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Mar, 2024 Read the published version in Language Resources and Evaluation → Version 1 posted 7 You are reading this latest preprint version Abstract This paper presents VeLeRo, an inflected lexicon of Standard Romanian which contains the full paradigm of 7297 verbs in phonological form. We explain the process by which the resource was compiled, and how stress, diphthongs and hiatus, consonant palatalization, and other relevant issues were handled in phonemization. On the basis of the most token-frequent verbs in VeLeRo, we also perform a quantitative analysis of morphological predictability in Romanian verbs, whose complexity patterns are presented within the broader Romance context. Romanian paradigm verb inflected lexicon morphological predictability entropy Figures Figure 1 Figure 2 Figure 3 1 Introduction Morphological predictability relations in paradigms have been explored for a long time. In classical language pedagogy, principal parts allowed learners of Latin, for example, to accurately predict any inflected form of a lexeme's paradigm from a small subset of word forms (see Finkel & Stump 2009). Morphologists have long known, thus, that "one inflection tends to predict another" (Matthews 1991 :97), so that Lat. templum templum predicts GEN.SG templī , NOM.PL templa , etc. Computational and theoretical advances in the last few decades, however, have enabled more exhaustive, systematic, and faster analyses of inflectional systems than ever before. In recent years, the complexity of inflectional systems has started to be analyzed more and more often from the perspective of the Paradigm Cell Filling Problem (PCFP, Ackerman et al. 2009 ), which is the name given to the challenge that speakers of languages with inflection classes face to produce essentially any form of any lexeme, no matter how infrequent the lemma or paradigm cell. Although high frequency forms could be stored in memory and learned by rote, given the rank frequencies of words in natural languages (Zipf 1932 ), with a long tail of extremely low or hapax (i.e. single-occurrence) words, most inflected words must be built online, with their forms predicted by language users on the basis of other more frequent forms in their paradigm, and/or on the basis of the equivalent forms or patterns as encountered in other more frequent lexemes. The methods to explore these morphological predictability relations within inflectional or derivational paradigms have been expanding in recent years. Traditional philological- comparative qualitative methods (e.g. Malkiel 1966 , Maiden 1992 , 2018 , O'Neill 2018, Esher 2022 , Herce 2022 ) continue to be pursued alongside quantitative computational ones involving Set Theory, Graph Theory, Machine learning, and Information Theory (e.g. Ackerman & Malouf 2013, Stump & Finkel 2013, Malouf 2017 , Beniamine 2018 , Elsner et al. 2019 , Sims 2020 ). Entropy (a measure of uncertainty, Shannon 1948) is the key notion of Information Theory. Applied to paradigmatic forms and the PCFP, conditional entropy can be used to measure the (un)predictability of one form given another. Software has also been developed to calculate different complexity measures automatically over whole systems: Beniamine's Qumin ( https://sacha.beniamine.net/software/qumin/ ) , Finkel's Principal Parts Analyzer ( https://www.cs.uky.edu/~raphael/linguistics/analyze.html ) , and Sims' Inflectional Networks scripts ( https://github.com/sims120/inflectional-networks ) among others. Alongside these methodological, theoretical, and software solutions, progress has also been made on the side of resources and databases (see e.g. Unimorph, Kirov et al. 2018). Most crucial to the PCFP are inflected lexicons, where all inflected forms of hundreds or thousands of lemmas are listed, preferably in phonological form. This is the data that is required for the quantitative investigation of morphological predictability and complexity. Most underdocumented at present, and hence most urgent to describe, are inflectional systems of non-Indo-European and non-WEIRD (Henrich et al. 2010 ), low-resource languages (Malouf et al. 2020 ). Better (i.e. larger, phonemized, well-curated) resources, and comparable morphological predictability analyses, however, are also needed for many national standard Indo-European languages. Focusing on Romance, arguably the language family where more attention has been paid to paradigmatic morphology, we have some family-wide but comparatively small inflected lexicons (see Maiden et al. 2010, Beniamine et al. 2020 ), as well as large lexicons and PCFP-analyses for some of the major languages in the family (namely French [Bonami et al. 2014 ], Latin [Pellegrini & Passarotti 2018 ], Italian [Pellegrini & Cignarella 2020], Portuguese [Beniamine et al. 2021 ], and Spanish [Herce 2023 ]). No such inflected lexicon and analysis exists, however, for Romanian. This is the purpose of the present paper. Section 2 will explain the creation of a verbal inflected lexicon for Romanian in phonological form (VeLeRo), annotated with lemma and cell frequencies. Section 3 presents a morphological predictability analysis of Romanian verbal inflection on the basis of this resource, and discusses the results briefly, particularly how they compare to extant analyses of other Romance languages. Section 4 summarizes the main highlights of the paper and proposes avenues for future research. 2 Building VeLeRo VeLeRo is an inflected lexicon of Romanian verbs in phonological form. It is based on Barbu’s ( 2008 ) lexical database RoMorphoDict, which contains the orthographical inflected forms, lemmas and morpho-syntactic descriptions of around 700,000 Romanian words. The verbal lemmas were extracted from this dataset and transcribed phonologically using Epitran ( https://github.com/dmort27/epitran ). Epitran is a massively multilingual, rule based G2P (grapheme to phoneme) system with support for 61 languages and distributed as open source software (a Python library) under an MIT license (Mortensen et al. 2018). Epitran’s rule-based conversion provided a broad phonemic transcription, but certain aspects required further refinement. Thus, the initial conversion was only used as the starting point for later adjustments. 2.1 Stress assignment In Romanian, stress can be oxytonic (final syllable), paroxytonic (penultimate syllable) or proparoxitonic (antepenultimate syllable) and is not marked orthographicaly. Some authors (Chitoran 1996 ) have claimed that stress is highly predictable, depending, among others, on the part of speech of the lexical item, but some others (Dindelegan 2013), have claimed that stress is largely unpredictable and highly mobile, especially in verbal inflections. RoMorphoDict was used as the point of departure for building the present resource because it indicates stress consistently on polysyllabic verbs. For consistency (i.e. to avoid spurious morphological contrasts), we added stress markers on monosyllabic verb forms as well (on the only vowel when a word had only one, or on the most open vowel in case the word contained a diphthong). 2.2 Diphtongs vs hiatuses Another important point when it comes to Romanian phonology is the representation of the diphtongs. According to Chitoran ( 2002 ) Romanian has two non-controversial diphthongs, namely /e̯a/ and /o̯a/. Apart from these, the glides /j/ and /w/ can combine with most vowels to create additional ones. For the creation of this lexicon, we adopted Chitoran’s ( 2002 ) treatment of diphthongs, where /j/ and /w/ have phonemic status and can be predicted by looking at syllable boundaries. Syllable boundary contrasts are not handled by Epitran, so these had to be encoded manually. For this, we used Barbu’s ( 2008 ) RoSyllabiDict database as a reference for the syllabification of verbs and made the necessary adjustments by hand. For example, in words such as “a.ban.do.nea.ză”, the sequence “ea” is homosyllabic, and was hence coded as a diphthong (e̯a). On the other hand, in words like “a.gre.a.ză”, were “e” and “a” are heterosyllabic, “ea” was transcribed as a sequence of vowels (ea). The lexicon contains a total of 16 diphthong sequences comprising different glide - vowel combinations plus /e̯a/ and /o̯a/. 2.3 Diphthong reduction Despite varying or inconsistent phonemic transcription practices elsewhere (e.g. in Maiden et al. 2010's Oxford Database of Romance Verb Morphology), sequences that involve postalveolars (e.g. /ʃ/) followed by /j/ or non-syllabic /e̯/ have been uniformly transcribed without this second segment here (e.g. /ziʧám/, rather than /ziʧe̯ám/, for ziceam 'say.1SG.IPF.IND'). This is justified by i) the absence of an audible front vowel in these sequences, ii) the absence of minimal pairs based on these sequences (for example /ʧe̯a/ vs /ʧja/ vs /ʧa/), and iii) by the phonemic transcription conventions in parallel cases in related Romance languages, for example Italian /diʧámo/, rather than *diʧjámo for diciamo 'say.1PL.PRS.IND', or Spanish /riɲó/ rather than *riɲjó for riñó 'scold.3SG.PST.IND' (note that in this latter case the standard spelling reflects pronunciation accurately). 2.4 Palatalization Palatalization is a prevalent feature of Romanian phonology and generally uncontested by most grammars. According to Chitoran ( 2002 :173), palatalization in Romanian can be phonologically or morphologically conditioned. The former refers to those instances where a change in articulation occurs automatically in a given phonetic environment (i.e. is allophonic). The latter refers to the cases where this environment has disappeared, thus leaving the palatalized consonant not predictable from its phonetic environment (i.e. phonemic). An instance of the first type is the palatalization of velars like /k/ and /g/ before a front vowel, where they are realized as [c] and [ɟ] (Dindelegan 2013) or [kʲ] and [gʲ] (Chitoran 2002 ) depending on the source. Because this inflected lexicon is aimed at capturing phonemic representations, automatic allophonic palatalization has not been represented. Only the latter type of palatalization (i.e. phonologized, unpredictable), thus, is relevant in the context of this inflected lexicon. The (orthographic) ending -i, occurs in the second person (singular and plural) in verbs, and often indicates a palatalization of a word-final consonant rather than a full word-final vowel /i/. Epitran, again, does not account for this, so modifications were necessary (e.g. to transcribe the form dați 'give.ind.prez.2p' as /dáʦʲ/ rather than [dáʦi]. Regarding this palatalization, the choice was made to distinguish between post-alveolar (e.g. /ʃ/) and post-alveolar palatalized (/ʃʲ/) phonemes. Although there is some research suggesting that these are extremely close acoustically (see Spinu et al. 2012 , 2019 ), most speakers appear to be able to distinguish the two sounds (Spinu 2018). Thus, a form like ziseși “say.2SG.PST” was transcribed as /ziséʃʲ/. A full vowel /i/ (also /u/ in the 1SG.PRS) has been preserved in Romanian pronunciation, and hence transcribed as 'i' here, only after segment sequences of increasing sonority (e.g. íntru 'I enter', íntri 'you enter', vs eksíst 'I exist', eksíʃtʲ 'you exist'). 2.5 Resource overview After all these steps, we arrived at a consistent phonemization of the complete paradigms of 7297 Romanian verbs, for a total of 284583 word forms. This is comparable (see Table 1 ) to the size of extant inflected lexicons in phonological form from other national standard Romance languages. Although this feature will not be used in the second part of this paper, our resource also includes overabundant (Thornton 2012 ) or substandard/dialectal forms (e.g. /fúrəm/ occurs, alongside standard /fusérəm/ as the ind.perf.1pl form of the verb fi 'be'). Given the importance of usage frequency for morphological learnability, predictability, and the PCFP, this information has also been supplied. The frequencies of lemmas in the corpus CoRoLa (Tufiș et al. 2019 ), a digital lemmatised corpus with over 1 billion words, have been added to VeLeRo and pattern in the way illustrated in Fig. 1 , with 9 verbs above 1 million tokens, 152 above 100,000, 727 above 10,000, 2146 above 1000, 4109 above 100, 5601 above 10, and 6688 verbs with at least 1 token in CoRoLa, while 609 verbs from our dataset, are completely unattested in that corpus. The frequency of paradigms cells (based also on CoRoLa) 1 is also shown in the right-hand panel of Fig. 1 . These also vary widely between the 3,152,827 tokens of the 3SG.PRS.IND (the most frequent value) and the 240 of the 2PL.IND.PFV (the least frequent one). Table 1 Size of VeLeRo and comparable inflected lexicons in other Romance varieties Language Reference Lemmas Word forms Latin Pellegrini & Passarotti 2018 3348 850392 French Bonami et al. 2014 4991 253174 Italian Pellegrini & Cignarella 2020 2053 108809 Portuguese Beniamine et al. 2021 4987 324214 Spanish VeLeSpa 6554 412839 Romanian VeLeRo, this paper 7297 284583 [1] Note that the frequencies of syncretic forms (e.g. 1SG.IND.IPF and 1PL.IND.IPF, as in /publikám/) are not distinguished in CoRoLa. Because we want to obtain a separate measure for each, we estimated individual paradigm-cell frequencies from the proportions observed between comparable person-number values in other tenses where these are not syncretic (e.g. in the PRS.IND: públik vs publikə́m). 3 A quantitative analysis of the PCFP in Romanian verbal inflection To keep computational times within reasonable limits, and to allow for comparability with extant quantitative analyses of the PCFP in other Romance languages, we decided to select for further analysis those (3564) verbs with 200 or more tokens in CoRoLa. This measure/threshold is justified due to two main reasons. The first is that many of the lower frequency verbs are unknown to even highly-educated native speakers of Romanian, and hence probably do not really form part of the average acquired inflectional system of the language, despite the presence of these verbs (and many others) in dictionaries and grammars that are understandably aimed at exhaustivity. The second is that this number of verbs is closer to the average number of items analyzed with identical methods in the extant literature on other Romance languages (see Bonami et al. 2014 , Pellegrini & Passarotti 2018 , Pellegrini & Cignarella 2020, Beniamine et al. 2021 , and Herce 2023 ), which will enable us to draw more meaningful cross-linguistic comparisons. After the mentioned exclusions (which included nonstandard overabundant forms), all remaining verbs and forms were analyzed in Qumin (Quantitative Modelling of Inflection, Beniamine 2018 ). This is a set of Python scripts that automatically extracts morphological alternations between all possible pairs of word forms in all lemmas. Due to the sheer number of combinations, 1482 (= 39*38) per verb, this is a task that can only be performed computationally. The algorithm finds maximally generalizable morphological alternations, 2 and derives a knowledge of which verbs show the same contrasts (i.e. belong to the same inflection class) and which have different ones (i.e. belong to different classes). Table 2 Extracted morphological alternations between two cells in eight verbs (A) lemma gloss 'INDPRS2SG' 'IMP2SG' ('INDPRS2SG', 'IMP2SG') da 'give' dáj də́ áj ⇌ ə́ lua 'take' jéj já éj ⇌ á forma 'form' formézʲ forme̯ázə ézʲ ⇌ e̯ázə facilita 'ease' faʧilitézʲ faʧilite̯ázə ézʲ ⇌ e̯ázə duce 'carry/lead' dúʧʲ dú ʧʲ ⇌ dumica 'chop up' dumíʧʲ dumíkə ʧʲ ⇌ kə împărți 'share' ɨmpárʦʲ ɨmpárte ʦʲ ⇌ te dăscăli 'teach' dəskəléʃtʲ dəskəléʃte tʲ ⇌ te Consider, as an illustrative example in Table 2 , some of the ways in which the 2SG present indicative can differ in Romanian from the 2SG imperative. While in some verbs ( forma and facilita ) these forms differ in identical ways (/ézʲ/ in the former value has to be replaced by /e̯ázə/ to form the latter and vice versa ), these forms contrast in different ways in other verbs (e.g. duce adds ʧʲ to the 2SG imperative to form the 2SG present indicative, but the same rule does not apply to derive the same form in dumica ). For the purposes of this pair of cells, thus, the former verbs (i.e. forma and facilita ) belong to the same class, while the latter verbs (i.e. duce and dumica ) belong to different morphological classes. To aid with sequence-to-sequence alignment, and the interpretability of morphological alternations, the Qumin algorithm also makes use of distinctive phonological features. A separate file needs to be supplied which defines the language's phonemes and their features (e.g. + or - voiced, + or - nasal, + or - velar, etc.). This file can be found, along with VeLeRo itself, on the supplementary information accompanying this paper ( https://osf.io/kqrjg/?view_only=80e7f5f9eeef4ccebb31ed882dccc7f7 ). As in the eight illustrative word-form pairs in Table 2 , patterns of morphological alternations are extracted for all word-form pairs of all verbs in our sample (1482*3564 = 5.3 million alternations). This is a demanding computational process that can last several hours. After they are extracted, the patterns can be inspected for quality control (making sure, for example, that infrequent or exceptional patterns are not due to mistakes or inconsistencies in either the original inflected lexicon or its subsequent phonemization. The extracted patterns also constitute the basis for various other scripts and functions within Qumin that allow to calculate further measures like conditional entropies, group inflection classes, etc. In the extant literature on the PCFP in Romance and beyond, the analysis of morphological predictability within paradigms often starts with a presentation of which values or word forms are mutually interpredictable with complete certainty. Thus, although the morphological relationship between some cells (e.g. INDPRS2SG and IMP2SG in Table 2 ) is a heterogeneous one, in the sense that it varies unpredictably from verb to verb, the morphological difference between other cells is the same across all verbs. This is the case, for example, of the INDIPFV1SG and INDIPFV2SG. As Table 3 shows, the former can be reliably transformed into the latter by replacing a word-final /m/ with /j/, and, conversely, the latter can be transformed into the former by changing this final /j/ into /m/. Table 3 Extracted morphological alternations between two cells in eight verbs (B) lemma gloss 'INDIPFV1SG' 'INDIPFV2SG' ('INDIPFV1SG', 'INDIPFV2SG') da 'give' dəde̯ám dəde̯áj m ⇌ j lua 'take' luám luáj m ⇌ j forma 'form' formám formáj m ⇌ j facilita 'ease' faʧilitám faʧilitáj m ⇌ j duce 'carry/lead' duʧám duʧáj m ⇌ j dumica 'chop up' dumikám dumikáj m ⇌ j împărți 'share' ɨmpərʦe̯ám ɨmpərʦe̯áj m ⇌ j dăscăli 'teach' dəskəle̯ám dəskəle̯áj m ⇌ j These paradigmatic domains of interpredictability, comparable to the notions of 'stem space' (see Montermini & Bonami 2013) or 'distillations' (Stump & Finkel 2013), provide a first measure of the complexity of an inflectional system. From the 1482 pairs of cells in a Romanian verbal paradigm, 236 (15.9%) involve no uncertainty (i.e. they have a conditional entropy of zero). In terms of concrete paradigm cells (see Fig. 2 ), the 39 cells of the Romanian verbal paradigm can be classified into 14 areas of interpredictability. As Fig. 2 shows, many of these areas (Z1, Z5, Z6, Z12, Z13, Z14) are single-cell ones (e.g. Z1 is the 2SG imperative) and hence trivial "areas" to some extent, since they merely indicate that some cells are not interpredictable with any other cell. A similar case is the one represented by those forms which contrast in values (e.g. 1SG present indicative and 1SG present subjunctive) but never in their form (dáw dáw, jáw jáw, forméz forméz, faʧilitéz faʧilitéz, dúk dúk, etc. for the verbs in Table 2 ). Systematic syncretisms like this account for areas Z3, Z4, and Z7. Remaining areas (i.e. Z2, Z8, Z9, Z10, and Z11) are the ones based on predictable morphological alternations like the one in Table 2 ). Notable commonalities can be identified between Romanian verbal inflection and that of the other major Romance languages analyzed with this same methodology to date (Beniamine 2018 , Pellegrini & Cignarella 2020, Beniamine et al. 2021 , and Herce 2023 ). The number of interpredictability areas (14 in Romanian, vs 15 in Italian [and Latin], 14 in Spanish and French, and 12 in Portuguese), and their distribution (e.g. most areas in the present indicative) are very similar to those found in other Romance languages. As in all other Romance languages analyzed so far, the 1SG present indicative, and the past participle constitute one-cell areas of their own. Some other aspects are shared with most but not all other Romance languages, for example, the fact that the 2SG imperative is also a one-cell area of its own (shared with all of Romance except Portuguese), or the fact that all the imperfective indicative cells constitute another area together, to the exclusion of all other cells (shared with all except French). Our results also show, of course, some differences to the patterns found in other Romance languages. A somewhat trivial one concerns the raw number of values a verb can inflect for, which is less in Romanian than in the other major Romance languages, mainly due to the absence of the synthetic future and conditional tenses, which did not emerge outside Western Romance (i.e. Portuguese, Spanish, French, Italian). In the cognate persons and tenses, another well-known difference is the one derived from the fact that the morphological subjunctives of the first and second person have been replaced by the corresponding indicative forms. This has generated a morphological overlap between present indicative and subjunctive not seen generally in other Romance languages. Regarding more aspects where there is within-Romance variation, Romanian patterns like Spanish and French (also like Latin), but unlike Italian or Portuguese, concerning the interpredictability of 1PL and 2PL present indicative. Regarding the absence of morphological interpredictability between the 2SG and the 3SG present indicative, and between the 1PL present indicative and the infinitive, Romanian patterns like Italian, and unlike Spanish and Portuguese. Although this goes beyond the goals of the present paper, these properties (e.g. whether any pair of cells is (1) or is not (0) mutually predictable) could be represented as vectors of (binary) values for each language, and between-language similarity could be explored via Hamming distances or similar (see Table 4 ) to check if paradigmatic structural distance corresponds to phylogenetic distance. Table 4: Presence (1) or absence (0) of morphological properties across Romance languages and Latin (left) , and between-language Hamming distances (right). A finer-grained approach to predictability reveals that some of these differences are not so categorical. Although 2SG and 3SG present indicative, and 1PL present indicative and the infinitive, are not perfectly interpredictable in Romanian as they are in other Romance languages, these forms are still very close to perfect predictability (see Table 5). Table 5: Conditional entropies (column given row) between the distillations in Figure 2. The average implicative entropy between Romanian cells is overall 0.1467 (0.1804 between distillations), which is slightly lower than in the other Romance languages that have been analyzed in a comparable way except Spanish: 0.28 for Latin (Pellegrini & Passarotti 2018), 0.18 for French, and 0.17 for Portuguese (Beniamine 2018), 0.07 for Spanish (Herce 2023). Conditional entropies between Romanian verb distillations differ widely, between 1.172 as the highest (the uncertainty involved in predicting Z6 (3PL.PRS.IND) from Z3 (the 1SG.PRS.IND) and 0 as the lowest. Closest to perfect interpredictability are Z5 and Z6, i.e. 3SG and 3PL present, and the different areas within the former perfectum tenses (aka. PYTA in the literature on Romance stem alternations, see Maiden 2001), e.g. Z9 and Z10, as well as these areas and the participle (i.e. Z14). Table 6: Some present indicative inflected forms and alternations in Romanian Table 6 illustrates some of the high-entropy (red) and low-entropy (green) alternations just mentioned, with the numbers in parentheses indicating the number of verbs (if >1) for which a particular alternation holds. Predicting the 3PL present indicative from the 1SG of the same tense is difficult (in this direction) because there are various frequent unpredictable ways in which the latter form can be turned into the former, most importantly ⇌ e (i.e. add /e/), ⇌ ə (i.e. add /ə/), and ⇌ (i.e. leave unchanged). Predictions in the opposite direction (i.e. predicting the 1SG form from the 3SG) are easier, and the same applies to predictions between 3SG and 3PL present indicative, because most forms (e.g. ʤe ⇌ g, ʧe ⇌ k, ʃte ⇌ sk) allow a speaker to deduce one from the other. Note that, among the alternations shown, only ⇌ and e ⇌ overlap in forms, which means a 'zero' 3PL does not fully diagnose the 3SG, which could be formed either with 'zero' (the most frequent option), or by adding -e. This is the reason why, as shown in Table 5, the conditional entropy of the 3PL given the 3SG is 0 (no uncertainty) but that of the 3SG given the 3PL is not zero (although still very low). Table 6: Average predictability and predictiveness of Romanian distillations Table 6 shows the average predictability and predictiveness across all distillations. Unlike in other Romance languages, it can be observed that differences in predictiveness are similar in range to differences in predictability, ranging roughly between 0.5 and almost zero. In common with other Romance languages, however, we can see quite a sharp boundary between the most and least predictable distillations. The zones Z1, Z3, Z5, Z6 and Z7 are all quite difficult to predict from other forms (see also Table 4 ). This is due to unpredictable stem alternations that separate rhyzotonic forms (the so-called N-morphome cells, see Maiden 2018 ) from arhyzotonic ones. Consider for example 1PL.PRS.IND.beg rugə́m > 1SG.PRS.IND róg, but 'occupy' okupə́m > okúp and 'calculate' kalkulə́m > kalkuléz. Looking at predictiveness, in turn, we find that the imperfect indicatives are clearly the forms which is least informative about the morphology of paradigm cells from other distillations. This is due to the very low allomorphic diversity of these forms compared to others, since the only lexical-class distinction that applies to them is the contrast between a suffix -a (for first conjugation verbs) and a suffix -e̯a (for all others). Beyond Romance, Romanian behaves like most (or maybe all) languages concerning the association between high frequency and irregularity (Herce 2019 , Wu et al. 2019 ). This can be observed (see Fig. 3 ) both at the lexeme level, where verbs from smaller classes tend to be more frequent (see Fig. 3 a) and at the paradigm cell level, where cells that belong to small or single-cell interpredictability domains (e.g. Z1, Z5, Z6, etc. in Fig. 2 ) tend to be more frequent than more regular cells (see e.g. Table 3 ), i.e. cells that can be predicted from multiple other ones in the paradigm. [2] For more detailed explanation of how the alternations are identified, for example when multiple descriptions are possible, see Beniamine et al. 2021. 4 Conclusion This paper has presented a new resource VeLeRo (Verbal Lexicon of Romanian), containing the full paradigms in phonological form of 7297 verbs, as well as the frequencies of these lemmas and of their different inflectional values. The lexicon is made openly available for further research into quantitative morphology and the PCFP. After outlining the overall interest of this resource and what role it can have within the general morphological-predictability literature in Section one, we proceeded to explain in Section 2 all steps and challenges that were involved into the resource's compilation: the addition of stress (in a language whose orthography does not indicate it), the phonemization of problematic cases like palatalizations and diphthongs, etc. In Section 3 , in turn, we used our resource and extant freely-available software (Beniamine's [2018] Qumin), to conduct an initial assessment on morphological predictive complexity in the system. The measures and results have been contextualized within the overall Romance landscape. Romanian is widely regarded as the most divergent Romance national standard language due to its earlier phylogenetic split (Balkan Romance split off from Western Romance [Port, Sp, Fr, It] before this group of languages started to break up), the lack of geographic contiguity to the rest of the Romance world, and strong language contact with Slavic languages. Although paradigm-structural differences between Romanian and its Romance sisters might correlate with geographic and phylogenetic distances (see Table 4 ), our results illustrate a great degree of similarity overall to the other Romance languages, with a comparable level of complexity (similar number and pattern of distillations, and average conditional entropies). Differences are found mostly on those aspects where variation exists already within Western Romance (e.g. regarding the morphological-predictive allegiance of the 1PL and 2PL present indicative, infinitive, of 2SG and 3SG or the present indicative, etc.) and tend to be a matter of degree. Overall, hence, the results point to the diachronic stability of morphological predictive relations within inflectional paradigms. This goes in line with the conservative nature that has been often claimed for inflectional morphology (Meillet 1958 , Nichols 1996 ), which is considered to be less prone to borrowing/contact than other components of language (Matras 2015 ). This should be particularly the case for structures like inflection classes and stem alternations which do not bear a direct relationship with meaning/function (Maiden 2018 ). If the conservativeness of paradigmatic predictability structure is confirmed, this would open the door to the possibility of using the paradigmatic complexity and patterns themselves for phylogenetic purposes (see Herce & Bickel forthcoming), i.e. to diagnose genetic relations between languages or inflectional systems and categories. This should be the focus of future research, along with the creation of large and well-curated inflected lexicons for other languages, particularly minoritized non-WEIRD languages (see e.g. Cruz et al. 2020 ), which might differ importantly with respect to the larger languages we are more familiar with (see e.g. Trudgill 2011 ). Declarations The author has no relevant financial or non-financial interests to disclose. Acknowledgements Funding No special funding associated with this research Authors contribution B.H. had the original idea and wrote the manuscript, B.P. led phonemization and computational analysis. All authors reviewed the manuscript. 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The fine implicative structure of European Portuguese conjugation." Isogloss. Open Journal of Romance Linguistics , 7 , 1–35. Bonami, O., Caron, G., Clément, & Plancq (2014). "Construction d'un lexique flexionnel phonétisé libre du français." In SHS Web of Conferences , vol. 8, 2583–2596. EDP Sciences. Chitoran, I. (1996). Prominence vs. rhythm: The predictability of stress in Romanian." Amsterdam studies in the theory and history of linguistic science. Series , 4 , 47–58. Chitoran, I. (2002). "The phonology of Romanian: a constraint-based approach." Studies in Generative Grammar, Vol. 56, 105–113. Berlin & New York: Mouton de Gruyter. Cruz, H., Stump, G., & Anastasopoulos, A. (2020). "A resource for studying chatino verbal morphology." arXiv preprint arXiv:2004.02083. Dindelegan, G., Pană, M., & Maiden (2013). "The grammar of Romanian." Oxford University Press. Elsner, M., Sims, A. D., Erdmann, A., Hernandez, A., Jaffe, E., Jin, L., Johnson, M. B. (2019). "Modelling morphological learning, typology, and change: What can the neural sequence-to-sequence framework contribute?" Journal of Language Modelling , 53–98. Esher, L. (2022). Overlapping subjunctive forms in Gallo- and Ibero-Romance verb paradigms. Revue Romane , 57 (1), 86–115. Finkel, R., and Gregory Stump (2009). What your teacher told you is true: Latin verbs have four principal parts. Digital Humanities Quarterly , 3 (1), 10–31826. Henrich, J., Heine, S. J., & Ara Norenzayan (2010). "Most people are not WEIRD " Nature 466, no. 7302: 29–29. Herce, B. (2019). "Deconstructing (ir)regularity." Studies in Language 43, no. 1: 44–91. Herce, B. (2022). Stress and stem allomorphy in the Romance perfectum: emergence, typology, and motivations of a symbiotic relation. Linguistics , 60 (4), 1103–1147. Herce, B. (2023). "VeLeSpa: An inflected verbal lexicon of Peninsular Spanish and a quantitative analysis of paradigmatic predictability.". Herce, B., & Bickel, B.. Forthcoming. "Paradigmatic predictability metrics as signals of phylogenetic relatedness: a proof of concept in Romance and Pamean diachrony.". Maiden, M. (2001). A strange affinity: ‘perfecto y tiempos afines’. Bulletin of Hispanic Studies , 78 (4), 441–464. Malkiel, Y. (1966). Diphthongization, monophthongization, metaphony: Studies in their interaction in the paradigm of the Old Spanish-ir verbs. Language , 42 (2), 430–472. Malouf, R. (2017). Abstractive morphological learning with a recurrent neural network. Morphology , 27 , 431–458. Malouf, R., Farrell, Ackerman, & Semenuks, A. (2020). "Lexical databases for computational analyses: A linguistic perspective." Proceedings of the Society for Computation in Linguistics 3, no. 1: 297–307. Maiden, M. (1992). Irregularity as a determinant of morphological change. Journal of Linguistics , 28 (2), 285–312. Maiden, M. (2018). The Romance verb: Morphomic structure and diachrony . Oxford University Press. Maiden, M., John Charles Smith, & Maria Goldbach. (2010). ; ; Silvio Cruschina; Marc-Olivier Hinzelin; &. "Oxford Online Database of Romance Verb Morphology." University of Oxford. Online at http://romverbmorph.clp.ox.ac.uk/ . Matras, Y. (2015). "Why is the borrowing of inflectional morphology dis-preferred?" In Borrowed Morphology, edited by Francesco Gardani, Peter Arkadiev & Nino Amiridze, 47–80. Berlin: De Gruyter. Matthews, P. H. (1991). "Morphology." Cambridge University Press. Meillet, A. (1958). Linguistique historique et linguistique générale." Société Linguistique de Paris, Collection Linguistique (8.). Paris: Librairie Honoré Champion. Mortensen, D. R., & Dalmia, S. (2018). and Patrick Littell. May. "Epitran: Precision G2P for many languages." In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). Nichols, J. (1996). "The Comparative Method as heuristic." In The Comparative Method Reviewed: Regularity and Irregularity in Language Change, edited by Mark Durie and Malcolm Ross, 39–71. O’Neill, P. (2018). "Velar allomorphy in Ibero-Romance." Studies in historical Ibero-Romance morpho-syntax 16: 13–43. Pellegrini, M., & Passarotti, M. (2018). "LatInfLexi: an inflected lexicon of Latin verbs." In Proceedings of the Fifth Italian Conference on Computational Linguistics (CLiC-it 2018) , 324–329. Accademia University Press. Pellegrini, M., & Alessandra Teresa Cignarella. (2020). &. "(Stem and Word) Predictability in Italian verb paradigms: An Entropy-Based Study Exploiting the New Resource LeFFI." In Proceedings of the 7th Italian Conference on Computational Linguistics (CLiC-it 2020): 1–6. CEUR. Sims, A. D. (2020). "Inflectional networks: Graph-theoretic tools for inflectional typology." Proceedings of the Society for Computation in Linguistics 3, no. 1: 88–98. Spinu, L., Vogel, I., & Timothy Bunnell, H. (2012). Palatalization in Romanian—Acoustic properties and perception. Journal of Phonetics , 40 (1), 54–66. Spinu, L., Percival, M., & Kochetov, A. (2019). "Articulatory Characteristics of Secondary Palatalization in Romanian Fricatives." In Interspeech, 3307–3311. Stump, G., Raphael, A., & Finkel (2013). Morphological typology: From word to paradigm (138 vol.). Cambridge: Cambridge University Press. Thornton, A. M. (2012). Reduction and maintenance of overabundance. A case study on Italian verb paradigms. Word Structure , 5 , 2: 183–207. Trudgill, P. (2011). Sociolinguistic typology: Social determinants of linguistic complexity . Oxford: Oxford University Press. Tufiș, D., Mititelu, V. B., Irimia, E., Păiș, V., & Ion, R. (2019). Nils Diewald, Maria Mitrofan, and Mihaela Onofrei. "Little strokes fell great oaks: creating CoRoLa, the reference corpus of contemporary Romanian.". Wu, S., Cotterell, R., Timothy, J., & O'Donnell (2019). "Morphological irregularity correlates with frequency." arXiv preprint arXiv:1906.11483. Zipf, G. K. (1932). "Selected studies of the principle of relative frequency in language.". Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Mar, 2024 Read the published version in Language Resources and Evaluation → Version 1 posted Editorial decision: Major revision 09 Oct, 2023 Reviews received at journal 17 Sep, 2023 Reviewers agreed at journal 31 Aug, 2023 Reviewers invited by journal 30 Aug, 2023 Editor assigned by journal 30 Aug, 2023 Submission checks completed at journal 16 Jul, 2023 First submitted to journal 13 Jul, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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In classical language pedagogy, principal parts allowed learners of Latin, for example, to accurately predict any inflected form of a lexeme's paradigm from a small subset of word forms (see Finkel \u0026amp; Stump 2009). Morphologists have long known, thus, that \"one inflection tends to predict another\" (Matthews \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1991\u003c/span\u003e:97), so that Lat. \u003cem\u003etemplum templum\u003c/em\u003e predicts GEN.SG \u003cem\u003etemplī\u003c/em\u003e, NOM.PL \u003cem\u003etempla\u003c/em\u003e, etc. Computational and theoretical advances in the last few decades, however, have enabled more exhaustive, systematic, and faster analyses of inflectional systems than ever before.\u003c/p\u003e \u003cp\u003eIn recent years, the complexity of inflectional systems has started to be analyzed more and more often from the perspective of the Paradigm Cell Filling Problem (PCFP, Ackerman et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), which is the name given to the challenge that speakers of languages with inflection classes face to produce essentially any form of any lexeme, no matter how infrequent the lemma or paradigm cell. Although high frequency forms could be stored in memory and learned by rote, given the rank frequencies of words in natural languages (Zipf \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1932\u003c/span\u003e), with a long tail of extremely low or hapax (i.e. single-occurrence) words, most inflected words must be built online, with their forms predicted by language users on the basis of other more frequent forms in their paradigm, and/or on the basis of the equivalent forms or patterns as encountered in other more frequent lexemes.\u003c/p\u003e \u003cp\u003eThe methods to explore these morphological predictability relations within inflectional or derivational paradigms have been expanding in recent years. Traditional philological- comparative qualitative methods (e.g. Malkiel \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1966\u003c/span\u003e, Maiden \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1992\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, O'Neill 2018, Esher \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Herce \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) continue to be pursued alongside quantitative computational ones involving Set Theory, Graph Theory, Machine learning, and Information Theory (e.g. Ackerman \u0026amp; Malouf 2013, Stump \u0026amp; Finkel 2013, Malouf \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Beniamine \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Elsner et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Sims \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Entropy (a measure of uncertainty, Shannon 1948) is the key notion of Information Theory. Applied to paradigmatic forms and the PCFP, conditional entropy can be used to measure the (un)predictability of one form given another. Software has also been developed to calculate different complexity measures automatically over whole systems: Beniamine's Qumin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sacha.beniamine.net/software/qumin/\u003c/span\u003e\u003cspan address=\"https://sacha.beniamine.net/software/qumin/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, Finkel's Principal Parts Analyzer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cs.uky.edu/~raphael/linguistics/analyze.html\u003c/span\u003e\u003cspan address=\"https://www.cs.uky.edu/~raphael/linguistics/analyze.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and Sims' Inflectional Networks scripts (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/sims120/inflectional-networks\u003c/span\u003e\u003cspan address=\"https://github.com/sims120/inflectional-networks\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e among others.\u003c/p\u003e \u003cp\u003eAlongside these methodological, theoretical, and software solutions, progress has also been made on the side of resources and databases (see e.g. Unimorph, Kirov et al. 2018). Most crucial to the PCFP are inflected lexicons, where all inflected forms of hundreds or thousands of lemmas are listed, preferably in phonological form. This is the data that is required for the quantitative investigation of morphological predictability and complexity. Most underdocumented at present, and hence most urgent to describe, are inflectional systems of non-Indo-European and non-WEIRD (Henrich et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), low-resource languages (Malouf et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Better (i.e. larger, phonemized, well-curated) resources, and comparable morphological predictability analyses, however, are also needed for many national standard Indo-European languages. Focusing on Romance, arguably the language family where more attention has been paid to paradigmatic morphology, we have some family-wide but comparatively small inflected lexicons (see Maiden et al. 2010, Beniamine et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as well as large lexicons and PCFP-analyses for some of the major languages in the family (namely French [Bonami et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e], Latin [Pellegrini \u0026amp; Passarotti \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e], Italian [Pellegrini \u0026amp; Cignarella 2020], Portuguese [Beniamine et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], and Spanish [Herce \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e]). No such inflected lexicon and analysis exists, however, for Romanian. This is the purpose of the present paper. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e will explain the creation of a verbal inflected lexicon for Romanian in phonological form (VeLeRo), annotated with lemma and cell frequencies. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a morphological predictability analysis of Romanian verbal inflection on the basis of this resource, and discusses the results briefly, particularly how they compare to extant analyses of other Romance languages. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the main highlights of the paper and proposes avenues for future research.\u003c/p\u003e"},{"header":"2 Building VeLeRo","content":"\u003cp\u003eVeLeRo is an inflected lexicon of Romanian verbs in phonological form. It is based on Barbu\u0026rsquo;s (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) lexical database RoMorphoDict, which contains the orthographical inflected forms, lemmas and morpho-syntactic descriptions of around 700,000 Romanian words. The verbal lemmas were extracted from this dataset and transcribed phonologically using Epitran (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/dmort27/epitran\u003c/span\u003e\u003cspan address=\"https://github.com/dmort27/epitran\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Epitran is a massively multilingual, rule based G2P (grapheme to phoneme) system with support for 61 languages and distributed as open source software (a Python library) under an MIT license (Mortensen et al. 2018). Epitran\u0026rsquo;s rule-based conversion provided a broad phonemic transcription, but certain aspects required further refinement. Thus, the initial conversion was only used as the starting point for later adjustments.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Stress assignment\u003c/h2\u003e \u003cp\u003eIn Romanian, stress can be oxytonic (final syllable), paroxytonic (penultimate syllable) or proparoxitonic (antepenultimate syllable) and is not marked orthographicaly. Some authors (Chitoran \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) have claimed that stress is highly predictable, depending, among others, on the part of speech of the lexical item, but some others (Dindelegan 2013), have claimed that stress is largely unpredictable and highly mobile, especially in verbal inflections. RoMorphoDict was used as the point of departure for building the present resource because it indicates stress consistently on polysyllabic verbs. For consistency (i.e. to avoid spurious morphological contrasts), we added stress markers on monosyllabic verb forms as well (on the only vowel when a word had only one, or on the most open vowel in case the word contained a diphthong).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Diphtongs vs hiatuses\u003c/h2\u003e \u003cp\u003eAnother important point when it comes to Romanian phonology is the representation of the diphtongs. According to Chitoran (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) Romanian has two non-controversial diphthongs, namely /e̯a/ and /o̯a/. Apart from these, the glides /j/ and /w/ can combine with most vowels to create additional ones. For the creation of this lexicon, we adopted Chitoran\u0026rsquo;s (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) treatment of diphthongs, where /j/ and /w/ have phonemic status and can be predicted by looking at syllable boundaries. Syllable boundary contrasts are not handled by Epitran, so these had to be encoded manually. For this, we used Barbu\u0026rsquo;s (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) RoSyllabiDict database as a reference for the syllabification of verbs and made the necessary adjustments by hand. For example, in words such as \u0026ldquo;a.ban.do.nea.ză\u0026rdquo;, the sequence \u0026ldquo;ea\u0026rdquo; is homosyllabic, and was hence coded as a diphthong (e̯a). On the other hand, in words like \u0026ldquo;a.gre.a.ză\u0026rdquo;, were \u0026ldquo;e\u0026rdquo; and \u0026ldquo;a\u0026rdquo; are heterosyllabic, \u0026ldquo;ea\u0026rdquo; was transcribed as a sequence of vowels (ea). The lexicon contains a total of 16 diphthong sequences comprising different glide - vowel combinations plus /e̯a/ and /o̯a/.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Diphthong reduction\u003c/h2\u003e \u003cp\u003eDespite varying or inconsistent phonemic transcription practices elsewhere (e.g. in Maiden et al. 2010's Oxford Database of Romance Verb Morphology), sequences that involve postalveolars (e.g. /ʃ/) followed by /j/ or non-syllabic /e̯/ have been uniformly transcribed without this second segment here (e.g. /ziʧ\u0026aacute;m/, rather than /ziʧe̯\u0026aacute;m/, for \u003cem\u003eziceam\u003c/em\u003e 'say.1SG.IPF.IND'). This is justified by i) the absence of an audible front vowel in these sequences, ii) the absence of minimal pairs based on these sequences (for example /ʧe̯a/ vs /ʧja/ vs /ʧa/), and iii) by the phonemic transcription conventions in parallel cases in related Romance languages, for example Italian /diʧ\u0026aacute;mo/, rather than *diʧj\u0026aacute;mo for \u003cem\u003ediciamo\u003c/em\u003e 'say.1PL.PRS.IND', or Spanish /riɲ\u0026oacute;/ rather than *riɲj\u0026oacute; for \u003cem\u003eri\u0026ntilde;\u0026oacute;\u003c/em\u003e 'scold.3SG.PST.IND' (note that in this latter case the standard spelling reflects pronunciation accurately).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Palatalization\u003c/h2\u003e \u003cp\u003ePalatalization is a prevalent feature of Romanian phonology and generally uncontested by most grammars. According to Chitoran (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e:173), palatalization in Romanian can be phonologically or morphologically conditioned. The former refers to those instances where a change in articulation occurs automatically in a given phonetic environment (i.e. is allophonic). The latter refers to the cases where this environment has disappeared, thus leaving the palatalized consonant not predictable from its phonetic environment (i.e. phonemic). An instance of the first type is the palatalization of velars like /k/ and /g/ before a front vowel, where they are realized as [c] and [ɟ] (Dindelegan 2013) or [kʲ] and [gʲ] (Chitoran \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) depending on the source. Because this inflected lexicon is aimed at capturing phonemic representations, automatic allophonic palatalization has not been represented. Only the latter type of palatalization (i.e. phonologized, unpredictable), thus, is relevant in the context of this inflected lexicon. The (orthographic) ending -i, occurs in the second person (singular and plural) in verbs, and often indicates a palatalization of a word-final consonant rather than a full word-final vowel /i/. Epitran, again, does not account for this, so modifications were necessary (e.g. to transcribe the form \u003cem\u003edați\u003c/em\u003e 'give.ind.prez.2p' as /d\u0026aacute;ʦʲ/ rather than [d\u0026aacute;ʦi]. Regarding this palatalization, the choice was made to distinguish between post-alveolar (e.g. /ʃ/) and post-alveolar palatalized (/ʃʲ/) phonemes. Although there is some research suggesting that these are extremely close acoustically (see Spinu et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), most speakers appear to be able to distinguish the two sounds (Spinu 2018). Thus, a form like \u003cem\u003eziseși\u003c/em\u003e \u0026ldquo;say.2SG.PST\u0026rdquo; was transcribed as /zis\u0026eacute;ʃʲ/. A full vowel /i/ (also /u/ in the 1SG.PRS) has been preserved in Romanian pronunciation, and hence transcribed as 'i' here, only after segment sequences of increasing sonority (e.g. \u0026iacute;ntru 'I enter', \u0026iacute;ntri 'you enter', vs eks\u0026iacute;st 'I exist', eks\u0026iacute;ʃtʲ 'you exist').\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Resource overview\u003c/h2\u003e \u003cp\u003eAfter all these steps, we arrived at a consistent phonemization of the complete paradigms of 7297 Romanian verbs, for a total of 284583 word forms. This is comparable (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to the size of extant inflected lexicons in phonological form from other national standard Romance languages. Although this feature will not be used in the second part of this paper, our resource also includes overabundant (Thornton \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) or substandard/dialectal forms (e.g. /f\u0026uacute;rəm/ occurs, alongside standard /fus\u0026eacute;rəm/ as the ind.perf.1pl form of the verb \u003cem\u003efi\u003c/em\u003e 'be'). Given the importance of usage frequency for morphological learnability, predictability, and the PCFP, this information has also been supplied. The frequencies of lemmas in the corpus CoRoLa (Tufiș et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a digital lemmatised corpus with over 1\u0026nbsp;billion words, have been added to VeLeRo and pattern in the way illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with 9 verbs above 1\u0026nbsp;million tokens, 152 above 100,000, 727 above 10,000, 2146 above 1000, 4109 above 100, 5601 above 10, and 6688 verbs with at least 1 token in CoRoLa, while 609 verbs from our dataset, are completely unattested in that corpus. The frequency of paradigms cells (based also on CoRoLa)\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e1\u003c/a\u003e is also shown in the right-hand panel of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. These also vary widely between the 3,152,827 tokens of the 3SG.PRS.IND (the most frequent value) and the 240 of the 2PL.IND.PFV (the least frequent one).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSize of VeLeRo and comparable inflected lexicons in other Romance varieties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLemmas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWord forms\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePellegrini \u0026amp; Passarotti \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e850392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrench\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBonami et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e253174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItalian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePellegrini \u0026amp; Cignarella 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePortuguese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeniamine et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e324214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVeLeSpa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e412839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRomanian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVeLeRo, this paper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e284583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e[1] Note that the frequencies of syncretic forms (e.g. 1SG.IND.IPF and 1PL.IND.IPF, as in /publik\u0026aacute;m/) are not distinguished in CoRoLa. Because we want to obtain a separate measure for each, we estimated individual paradigm-cell frequencies from the proportions observed between comparable person-number values in other tenses where these are not syncretic (e.g. in the PRS.IND: p\u0026uacute;blik vs publikə́m).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"3 A quantitative analysis of the PCFP in Romanian verbal inflection","content":"\u003cp\u003eTo keep computational times within reasonable limits, and to allow for comparability with extant quantitative analyses of the PCFP in other Romance languages, we decided to select for further analysis those (3564) verbs with 200 or more tokens in CoRoLa. This measure/threshold is justified due to two main reasons. The first is that many of the lower frequency verbs are unknown to even highly-educated native speakers of Romanian, and hence probably do not really form part of the average acquired inflectional system of the language, despite the presence of these verbs (and many others) in dictionaries and grammars that are understandably aimed at exhaustivity. The second is that this number of verbs is closer to the average number of items analyzed with identical methods in the extant literature on other Romance languages (see Bonami et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e, Pellegrini \u0026amp; Passarotti \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e, Pellegrini \u0026amp; Cignarella 2020, Beniamine et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, and Herce \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), which will enable us to draw more meaningful cross-linguistic comparisons.\u003c/p\u003e\n\u003cp\u003eAfter the mentioned exclusions (which included nonstandard overabundant forms), all remaining verbs and forms were analyzed in Qumin (Quantitative Modelling of Inflection, Beniamine \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). This is a set of Python scripts that automatically extracts morphological alternations between all possible pairs of word forms in all lemmas. Due to the sheer number of combinations, 1482 (=\u0026thinsp;39*38) per verb, this is a task that can only be performed computationally. The algorithm finds maximally generalizable morphological alternations,\u003ca id=\"#FNLinkFn2\" class=\"FNLink\" href=\"#Fn2\"\u003e2\u003c/a\u003e and derives a knowledge of which verbs show the same contrasts (i.e. belong to the same inflection class) and which have different ones (i.e. belong to different classes).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eExtracted morphological alternations between two cells in eight verbs (A)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003elemma\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003egloss\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e'INDPRS2SG'\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e'IMP2SG'\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e('INDPRS2SG', 'IMP2SG')\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eda\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'give'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edə́\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026aacute;j ⇌ ə́\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elua\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'take'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ej\u0026eacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ej\u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026eacute;j ⇌ \u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eforma\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'form'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eform\u0026eacute;zʲ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eforme̯\u0026aacute;zə\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026eacute;zʲ ⇌ e̯\u0026aacute;zə\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003efacilita\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'ease'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efaʧilit\u0026eacute;zʲ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efaʧilite̯\u0026aacute;zə\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026eacute;zʲ ⇌ e̯\u0026aacute;zə\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003educe\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'carry/lead'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u0026uacute;ʧʲ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ed\u0026uacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eʧʲ ⇌\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003edumica\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'chop up'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edum\u0026iacute;ʧʲ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edum\u0026iacute;kə\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eʧʲ ⇌ kə\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026icirc;mpărți\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'share'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eɨmp\u0026aacute;rʦʲ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eɨmp\u0026aacute;rte\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eʦʲ ⇌ te\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003edăscăli\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'teach'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edəskəl\u0026eacute;ʃtʲ\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edəskəl\u0026eacute;ʃte\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003etʲ ⇌ te\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eConsider, as an illustrative example in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, some of the ways in which the 2SG present indicative can differ in Romanian from the 2SG imperative. While in some verbs (\u003cem\u003eforma\u003c/em\u003e and \u003cem\u003efacilita\u003c/em\u003e) these forms differ in identical ways (/\u0026eacute;zʲ/ in the former value has to be replaced by /e̯\u0026aacute;zə/ to form the latter and \u003cem\u003evice versa\u003c/em\u003e), these forms contrast in different ways in other verbs (e.g. \u003cem\u003educe\u003c/em\u003e adds ʧʲ to the 2SG imperative to form the 2SG present indicative, but the same rule does not apply to derive the same form in \u003cem\u003edumica\u003c/em\u003e). For the purposes of this pair of cells, thus, the former verbs (i.e. \u003cem\u003eforma\u003c/em\u003e and \u003cem\u003efacilita\u003c/em\u003e) belong to the same class, while the latter verbs (i.e. \u003cem\u003educe\u003c/em\u003e and \u003cem\u003edumica\u003c/em\u003e) belong to different morphological classes. To aid with sequence-to-sequence alignment, and the interpretability of morphological alternations, the Qumin algorithm also makes use of distinctive phonological features. A separate file needs to be supplied which defines the language's phonemes and their features (e.g. + or - voiced, + or - nasal, + or - velar, etc.). This file can be found, along with VeLeRo itself, on the supplementary information accompanying this paper\u003c/p\u003e\n\u003cp\u003e(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/kqrjg/?view_only=80e7f5f9eeef4ccebb31ed882dccc7f7\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"Underline\"\u003e).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAs in the eight illustrative word-form pairs in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, patterns of morphological alternations are extracted for all word-form pairs of all verbs in our sample (1482*3564\u0026thinsp;=\u0026thinsp;5.3\u0026nbsp;million alternations). This is a demanding computational process that can last several hours. After they are extracted, the patterns can be inspected for quality control (making sure, for example, that infrequent or exceptional patterns are not due to mistakes or inconsistencies in either the original inflected lexicon or its subsequent phonemization. The extracted patterns also constitute the basis for various other scripts and functions within Qumin that allow to calculate further measures like conditional entropies, group inflection classes, etc.\u003c/p\u003e\n\u003cp\u003eIn the extant literature on the PCFP in Romance and beyond, the analysis of morphological predictability within paradigms often starts with a presentation of which values or word forms are mutually interpredictable with complete certainty. Thus, although the morphological relationship between some cells (e.g. INDPRS2SG and IMP2SG in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) is a heterogeneous one, in the sense that it varies unpredictably from verb to verb, the morphological difference between other cells is the same across all verbs. This is the case, for example, of the INDIPFV1SG and INDIPFV2SG. As Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows, the former can be reliably transformed into the latter by replacing a word-final /m/ with /j/, and, conversely, the latter can be transformed into the former by changing this final /j/ into /m/.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eExtracted morphological alternations between two cells in eight verbs (B)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003elemma\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003egloss\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e'INDIPFV1SG'\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e'INDIPFV2SG'\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e('INDIPFV1SG', 'INDIPFV2SG')\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eda\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'give'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edəde̯\u0026aacute;m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edəde̯\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003em ⇌ j\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003elua\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'take'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003elu\u0026aacute;m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003elu\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003em ⇌ j\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eforma\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'form'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eform\u0026aacute;m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eform\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003em ⇌ j\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003efacilita\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'ease'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efaʧilit\u0026aacute;m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003efaʧilit\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003em ⇌ j\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003educe\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'carry/lead'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eduʧ\u0026aacute;m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eduʧ\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003em ⇌ j\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003edumica\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'chop up'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edumik\u0026aacute;m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edumik\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003em ⇌ j\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026icirc;mpărți\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'share'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eɨmpərʦe̯\u0026aacute;m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eɨmpərʦe̯\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003em ⇌ j\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003edăscăli\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e'teach'\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edəskəle̯\u0026aacute;m\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edəskəle̯\u0026aacute;j\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003em ⇌ j\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThese paradigmatic domains of interpredictability, comparable to the notions of 'stem space' (see Montermini \u0026amp; Bonami 2013) or 'distillations' (Stump \u0026amp; Finkel 2013), provide a first measure of the complexity of an inflectional system. From the 1482 pairs of cells in a Romanian verbal paradigm, 236 (15.9%) involve no uncertainty (i.e. they have a conditional entropy of zero). In terms of concrete paradigm cells (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), the 39 cells of the Romanian verbal paradigm can be classified into 14 areas of interpredictability.\u003c/p\u003e\n\u003cp\u003eAs Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows, many of these areas (Z1, Z5, Z6, Z12, Z13, Z14) are single-cell ones (e.g. Z1 is the 2SG imperative) and hence trivial \"areas\" to some extent, since they merely indicate that some cells are not interpredictable with any other cell. A similar case is the one represented by those forms which contrast in values (e.g. 1SG present indicative and 1SG present subjunctive) but never in their form (d\u0026aacute;w d\u0026aacute;w, j\u0026aacute;w j\u0026aacute;w, form\u0026eacute;z form\u0026eacute;z, faʧilit\u0026eacute;z faʧilit\u0026eacute;z, d\u0026uacute;k d\u0026uacute;k, etc. for the verbs in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Systematic syncretisms like this account for areas Z3, Z4, and Z7. Remaining areas (i.e. Z2, Z8, Z9, Z10, and Z11) are the ones based on predictable morphological alternations like the one in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNotable commonalities can be identified between Romanian verbal inflection and that of the other major Romance languages analyzed with this same methodology to date (Beniamine \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e, Pellegrini \u0026amp; Cignarella 2020, Beniamine et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, and Herce \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The number of interpredictability areas (14 in Romanian, vs 15 in Italian [and Latin], 14 in Spanish and French, and 12 in Portuguese), and their distribution (e.g. most areas in the present indicative) are very similar to those found in other Romance languages. As in all other Romance languages analyzed so far, the 1SG present indicative, and the past participle constitute one-cell areas of their own. Some other aspects are shared with most but not all other Romance languages, for example, the fact that the 2SG imperative is also a one-cell area of its own (shared with all of Romance except Portuguese), or the fact that all the imperfective indicative cells constitute another area together, to the exclusion of all other cells (shared with all except French).\u003c/p\u003e\n\u003cp\u003eOur results also show, of course, some differences to the patterns found in other Romance languages. A somewhat trivial one concerns the raw number of values a verb can inflect for, which is less in Romanian than in the other major Romance languages, mainly due to the absence of the synthetic future and conditional tenses, which did not emerge outside Western Romance (i.e. Portuguese, Spanish, French, Italian). In the cognate persons and tenses, another well-known difference is the one derived from the fact that the morphological subjunctives of the first and second person have been replaced by the corresponding indicative forms. This has generated a morphological overlap between present indicative and subjunctive not seen generally in other Romance languages.\u003c/p\u003e\n\u003cp\u003eRegarding more aspects where there is within-Romance variation, Romanian patterns like Spanish and French (also like Latin), but unlike Italian or Portuguese, concerning the interpredictability of 1PL and 2PL present indicative. Regarding the absence of morphological interpredictability between the 2SG and the 3SG present indicative, and between the 1PL present indicative and the infinitive, Romanian patterns like Italian, and unlike Spanish and Portuguese.\u003c/p\u003e\n\u003cp\u003eAlthough this goes beyond the goals of the present paper, these properties (e.g. whether any pair of cells is (1) or is not (0) mutually predictable) could be represented as vectors of (binary) values for each language, and between-language similarity could be explored via Hamming distances or similar (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) to check if paradigmatic structural distance corresponds to phylogenetic distance.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cp\u003eTable 4: Presence (1) or absence (0) of morphological properties across Romance languages and Latin (left) , and between-language Hamming distances (right).\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA finer-grained approach to predictability reveals that some of these differences are not so categorical. Although 2SG and 3SG present indicative, and 1PL present indicative and the infinitive, are not perfectly interpredictable in Romanian as they are in other Romance languages, these forms are still very close to perfect predictability (see Table 5).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5: Conditional entropies (column given row) between the distillations in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe average implicative entropy between Romanian cells is overall 0.1467 (0.1804 between distillations), which is slightly lower than in the other Romance languages that have been analyzed in a comparable way except Spanish: 0.28 for Latin (Pellegrini \u0026amp; Passarotti 2018), 0.18 for French, and 0.17 for Portuguese (Beniamine 2018), 0.07 for Spanish (Herce 2023). Conditional entropies between Romanian verb distillations differ widely, between 1.172 as the highest (the uncertainty involved in predicting Z6 (3PL.PRS.IND) from Z3 (the 1SG.PRS.IND) and 0 as the lowest. Closest to perfect interpredictability are Z5 and Z6, i.e. 3SG and 3PL present, and the different areas within the former perfectum tenses (aka. PYTA in the literature on Romance stem alternations, see Maiden 2001), e.g. Z9 and Z10, as well as these areas and the participle (i.e. Z14).\u003c/p\u003e\n\u003cp\u003eTable 6: Some present indicative inflected forms and alternations in Romanian\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 illustrates some of the high-entropy (red) and low-entropy (green) alternations just mentioned, with the numbers in parentheses indicating the number of verbs (if \u0026gt;1) for which a particular alternation holds. Predicting the 3PL present indicative from the 1SG of the same tense is difficult (in this direction) because there are various frequent unpredictable ways in which the latter form can be turned into the former, most importantly ⇌ e (i.e. add /e/), ⇌ ə (i.e. add /ə/), and ⇌ (i.e. leave unchanged). Predictions in the opposite direction (i.e. predicting the 1SG form from the 3SG) are easier, and the same applies to predictions between 3SG and 3PL present indicative, because most forms (e.g. ʤe ⇌ g, ʧe ⇌ k,\u0026nbsp; ʃte ⇌ sk) allow a speaker to deduce one from the other. Note that, among the alternations shown, only ⇌ and e ⇌ overlap in forms, which means a 'zero' 3PL does not fully diagnose the 3SG, which could be formed either with 'zero' (the most frequent option), or by adding -e. This is the reason why, as shown in Table 5, the conditional entropy of the 3PL given the 3SG is 0 (no uncertainty) but that of the 3SG given the 3PL is not zero (although still very low).\u003c/p\u003e\n\u003cp\u003eTable 6: Average predictability and predictiveness of Romanian distillations\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" alt=\"\" /\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows the average predictability and predictiveness across all distillations. Unlike in other Romance languages, it can be observed that differences in predictiveness are similar in range to differences in predictability, ranging roughly between 0.5 and almost zero. In common with other Romance languages, however, we can see quite a sharp boundary between the most and least predictable distillations. The zones Z1, Z3, Z5, Z6 and Z7 are all quite difficult to predict from other forms (see also Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). This is due to unpredictable stem alternations that separate rhyzotonic forms (the so-called N-morphome cells, see Maiden \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) from arhyzotonic ones. Consider for example 1PL.PRS.IND.beg rugə́m\u0026thinsp;\u0026gt;\u0026thinsp;1SG.PRS.IND r\u0026oacute;g, but 'occupy' okupə́m\u0026thinsp;\u0026gt;\u0026thinsp;ok\u0026uacute;p and 'calculate' kalkulə́m\u0026thinsp;\u0026gt;\u0026thinsp;kalkul\u0026eacute;z. Looking at predictiveness, in turn, we find that the imperfect indicatives are clearly the forms which is least informative about the morphology of paradigm cells from other distillations. This is due to the very low allomorphic diversity of these forms compared to others, since the only lexical-class distinction that applies to them is the contrast between a suffix -a (for first conjugation verbs) and a suffix -e̯a (for all others).\u003c/p\u003e\n\u003cp\u003eBeyond Romance, Romanian behaves like most (or maybe all) languages concerning the association between high frequency and irregularity (Herce \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e, Wu et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). This can be observed (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) both at the lexeme level, where verbs from smaller classes tend to be more frequent (see Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea) and at the paradigm cell level, where cells that belong to small or single-cell interpredictability domains (e.g. Z1, Z5, Z6, etc. in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) tend to be more frequent than more regular cells (see e.g. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), i.e. cells that can be predicted from multiple other ones in the paradigm.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[2] For more detailed explanation of how the alternations are identified, for example when multiple descriptions are possible, see Beniamine et al. 2021.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis paper has presented a new resource VeLeRo (Verbal Lexicon of Romanian), containing the full paradigms in phonological form of 7297 verbs, as well as the frequencies of these lemmas and of their different inflectional values. The lexicon is made openly available for further research into quantitative morphology and the PCFP.\u003c/p\u003e \u003cp\u003eAfter outlining the overall interest of this resource and what role it can have within the general morphological-predictability literature in Section one, we proceeded to explain in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e all steps and challenges that were involved into the resource's compilation: the addition of stress (in a language whose orthography does not indicate it), the phonemization of problematic cases like palatalizations and diphthongs, etc. In Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in turn, we used our resource and extant freely-available software (Beniamine's [2018] Qumin), to conduct an initial assessment on morphological predictive complexity in the system. The measures and results have been contextualized within the overall Romance landscape. Romanian is widely regarded as the most divergent Romance national standard language due to its earlier phylogenetic split (Balkan Romance split off from Western Romance [Port, Sp, Fr, It] before this group of languages started to break up), the lack of geographic contiguity to the rest of the Romance world, and strong language contact with Slavic languages. Although paradigm-structural differences between Romanian and its Romance sisters might correlate with geographic and phylogenetic distances (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), our results illustrate a great degree of similarity overall to the other Romance languages, with a comparable level of complexity (similar number and pattern of distillations, and average conditional entropies). Differences are found mostly on those aspects where variation exists already within Western Romance (e.g. regarding the morphological-predictive allegiance of the 1PL and 2PL present indicative, infinitive, of 2SG and 3SG or the present indicative, etc.) and tend to be a matter of degree.\u003c/p\u003e \u003cp\u003eOverall, hence, the results point to the diachronic stability of morphological predictive relations within inflectional paradigms. This goes in line with the conservative nature that has been often claimed for inflectional morphology (Meillet \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1958\u003c/span\u003e, Nichols \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), which is considered to be less prone to borrowing/contact than other components of language (Matras \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This should be particularly the case for structures like inflection classes and stem alternations which do not bear a direct relationship with meaning/function (Maiden \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). If the conservativeness of paradigmatic predictability structure is confirmed, this would open the door to the possibility of using the paradigmatic complexity and patterns themselves for phylogenetic purposes (see Herce \u0026amp; Bickel forthcoming), i.e. to diagnose genetic relations between languages or inflectional systems and categories. This should be the focus of future research, along with the creation of large and well-curated inflected lexicons for other languages, particularly minoritized non-WEIRD languages (see e.g. Cruz et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which might differ importantly with respect to the larger languages we are more familiar with (see e.g. Trudgill \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe author has no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo special funding associated with this research\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB.H. had the original idea and wrote the manuscript, B.P. led phonemization and computational analysis. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAckerman, F., Blevins, J. P., \u0026amp; Robert Malouf (2009). \"Parts and wholes: Patterns of relatedness in complex morphological systems and why they matter.\" In Analogy in Grammar: Form and Acquisition, edited by James P. Blevins and Juliette Blevins, 54\u0026ndash;82. Oxford: Oxford University Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAckerman, F., \u0026amp; Robert Malouf (2013). \"Morphological organization: The low conditional entropy conjecture.\" Language 89, no. 3: 429\u0026ndash;464.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbu, A. M. (2008). May. \"Romanian Lexical Data Bases: Inflected and Syllabic Forms Dictionaries.\" In \u003cem\u003eLREC\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeniamine, S. 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(1932). \"Selected studies of the principle of relative frequency in language.\".\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"language-resources-and-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lrev","sideBox":"Learn more about [Language Resources and Evaluation](http://link.springer.com/journal/10579)","snPcode":"10579","submissionUrl":"https://submission.nature.com/new-submission/10579/3","title":"Language Resources and Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Romanian, paradigm, verb, inflected lexicon, morphological predictability, entropy","lastPublishedDoi":"10.21203/rs.3.rs-3167337/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3167337/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper presents VeLeRo, an inflected lexicon of Standard Romanian which contains the full paradigm of 7297 verbs in phonological form. We explain the process by which the resource was compiled, and how stress, diphthongs and hiatus, consonant palatalization, and other relevant issues were handled in phonemization. On the basis of the most token-frequent verbs in VeLeRo, we also perform a quantitative analysis of morphological predictability in Romanian verbs, whose complexity patterns are presented within the broader Romance context.\u003c/p\u003e","manuscriptTitle":"VeLeRo: An inflected verbal lexicon of Standard Romanian and a quantitative analysis of morphological predictability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-20 21:10:35","doi":"10.21203/rs.3.rs-3167337/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-10-09T11:26:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-17T18:44:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"839c83e9-2c86-4713-b919-849a178e425a","date":"2023-08-31T06:11:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-30T16:32:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-30T16:16:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-07-16T15:09:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Language Resources and Evaluation","date":"2023-07-13T13:05:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"language-resources-and-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lrev","sideBox":"Learn more about [Language Resources and Evaluation](http://link.springer.com/journal/10579)","snPcode":"10579","submissionUrl":"https://submission.nature.com/new-submission/10579/3","title":"Language Resources and Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a9d3d034-9aa1-4648-b91a-876f653ec204","owner":[],"postedDate":"July 20th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-25T15:08:58+00:00","versionOfRecord":{"articleIdentity":"rs-3167337","link":"https://doi.org/10.1007/s10579-024-09721-3","journal":{"identity":"language-resources-and-evaluation","isVorOnly":false,"title":"Language Resources and Evaluation"},"publishedOn":"2024-03-23 15:03:05","publishedOnDateReadable":"March 23rd, 2024"},"versionCreatedAt":"2023-07-20 21:10:35","video":"","vorDoi":"10.1007/s10579-024-09721-3","vorDoiUrl":"https://doi.org/10.1007/s10579-024-09721-3","workflowStages":[]},"version":"v1","identity":"rs-3167337","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3167337","identity":"rs-3167337","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}