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            Multiword expressions in lexical resources

            Linguistic, lexicographic, and computational perspectives

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            Contributor(s)
            Giouli, Voula (editor)
            Barbu Mititelu, Verginica (editor)
            Collection
            Knowledge Unlatched (KU)
            Language
            English
            Show full item record
            Abstract
            This volume contains chapters that paint the current landscape of the multiword expressions (MWE) representation in lexical resources, in view of their robust identification and computational processing. Both large-size general lexica and smaller MWE-centred ones are included, with special focus on the representation decisions and mechanisms that facilitate their usage in Natural Language Processing tasks. The presentations go beyond the morpho-syntactic description of MWEs, into their semantics. One challenge in representing MWEs in lexical resources is ensuring that the variability along with extra features required by the different types of MWEs can be captured efficiently. In this respect, recommendations for representing MWEs in mono- and multilingual computational lexicons have been proposed; these focus mainly on the syntactic and semantic properties of support verbs and noun compounds and their proper encoding thereof.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/176834
            Keywords
            Language Arts & Disciplines; Linguistics
            DOI
            https://doi.org/10.5281/zenodo.10949960
            Publisher
            Language Science Press
            Publisher website
            http://langsci-press.org/
            Publication date and place
            2024
            Grantor
            • Knowledge Unlatched
            Imprint
            Language Science Press
            Classification
            linguistics
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            Credits


            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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