A diagnostic and evaluative analysis of PARSEME corpora complexity

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Abstract Verbal multiword expressions (VMWEs) constitute a major source of structural complexity in multilingual corpora. While previous work has focused primarily on their identification performance and label distributions, less attention has been paid to the structural properties of the corpora themselves. This paper presents a multi-dimensional diagnostic of structural complexity in the PARSEME VMWE corpora, with the aim of providing a systematic basis for cross-linguistic comparison and for the development and evaluation of automatic VMWE identification systems. We analyse all available languages and annotation splits (train, development, and test) along four complementary dimensions: sentence-level properties, token-level properties, MWE-level properties, and inter-MWE relations. In particular, we introduce a formally defined taxonomy of inter-MWE patterns based on token-index relations, distinguishing positional, boundary-sharing, and identity-based patterns, while treating token sharing as a transversal structural property. Our results reveal substantial cross-lingual asymmetries in sentence length distributions, VMWE density, token sharing, and higher-order interaction patterns. Although most VMWEs occur in isolation, certain languages exhibit non-negligible proportions of overlapping and multi-relation patterns. We further show that structural properties are not always evenly distributed across annotation splits, which may have implications for system evaluation. By quantifying structural complexity across languages and annotation splits, this study contributes to corpus diagnostics and resource evaluation in multilingual MWE research. The proposed framework makes structural properties explicit, supports reproducible corpus profiling, and provides an empirical basis for interpreting cross-lingual benchmarking results. Beyond VMWE identification, the methodology offers a generalisable approach to evaluating structural complexity in annotated linguistic resources.
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A diagnostic and evaluative analysis of PARSEME corpora complexity | 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 Research Article A diagnostic and evaluative analysis of PARSEME corpora complexity Santiago Fernández Lanza, Víctor Manuel Darriba Bilbao, Daniel Fernández-González This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9023725/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Verbal multiword expressions (VMWEs) constitute a major source of structural complexity in multilingual corpora. While previous work has focused primarily on their identification performance and label distributions, less attention has been paid to the structural properties of the corpora themselves. This paper presents a multi-dimensional diagnostic of structural complexity in the PARSEME VMWE corpora, with the aim of providing a systematic basis for cross-linguistic comparison and for the development and evaluation of automatic VMWE identification systems. We analyse all available languages and annotation splits (train, development, and test) along four complementary dimensions: sentence-level properties, token-level properties, MWE-level properties, and inter-MWE relations. In particular, we introduce a formally defined taxonomy of inter-MWE patterns based on token-index relations, distinguishing positional, boundary-sharing, and identity-based patterns, while treating token sharing as a transversal structural property. Our results reveal substantial cross-lingual asymmetries in sentence length distributions, VMWE density, token sharing, and higher-order interaction patterns. Although most VMWEs occur in isolation, certain languages exhibit non-negligible proportions of overlapping and multi-relation patterns. We further show that structural properties are not always evenly distributed across annotation splits, which may have implications for system evaluation. By quantifying structural complexity across languages and annotation splits, this study contributes to corpus diagnostics and resource evaluation in multilingual MWE research. The proposed framework makes structural properties explicit, supports reproducible corpus profiling, and provides an empirical basis for interpreting cross-lingual benchmarking results. Beyond VMWE identification, the methodology offers a generalisable approach to evaluating structural complexity in annotated linguistic resources. Verbal multiword expressions Multilingual Natural Language Processing Corpus analysis Inter-MWE relations Token sharing PARSEME Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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