Papers by Gilles Sérasset

5 papers
Bridging Computational Lexicography and Corpus Linguistics: A Query Extension for OntoLex-FrAC (2024.lrec-main)

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Challenge: OntoLex is the dominant community standard for machine-readable lexical resources . it is currently extended with a designated module for Frequency, Attestations and Corpus-based Information .
Approach: They propose a module for Frequency, Attestations and Corpus-based Information for OntoLex . the module enables RDF-based web services to exchange corpus queries dynamically .
Outcome: The proposed module addresses the incorporation of corpus queries for linking dictionaries with corpus engines and enabling RDF-based web services to exchange corpus query data dynamically.
Cross-Lingual Link Discovery for Under-Resourced Languages (2022.lrec-1)

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Challenge: Linked data paradigms can be used to solve under-resourced languages' problem of under-utilization of resources.
Approach: They propose a paradigm for cross-lingual link discovery that can be applied to under-resourced languages . they argue that techniques for cross language linking can be readily applied .
Outcome: The proposed technologies can be applied to under-resourced languages, the authors argue . the authors show that the Linked Data paradigm can be used to solve the problem .
From Linguistic Linked Data to Big Data (2024.lrec-main)

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Challenge: Language data on the LOD cloud has grown in number, size, and variety . Linked (Open) Data (LLOD) is a standardized way of representing and sharing linguistic datasets .
Approach: They propose to combine LLOD and Big Data to improve interoperability of linguistic datasets . they propose to use a machine-readable format to represent and share linguistic data .
Outcome: This paper examines the use cases of Linked (Open) Data and Big Data in language data.
MultiLexBATS: Multilingual Dataset of Lexical Semantic Relations (2024.lrec-main)

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Challenge: Prior work has focused on analysing lexical semantic relations in word embeddings or probing pretrained language models (PLMs) with some exceptions.
Approach: They propose to use a multilingual parallel dataset of lexical semantic relations adapted from BATS in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian as an experiment on cross-lingual transfer of relational knowledge.
Outcome: The proposed dataset is adapted from a BATS-based dataset in 15 languages including low-resource languages such as Bambara, Lithuanian, and Albanian.
On Modelling Corpus Citations in Computational Lexical Resources (2024.lrec-main)

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Challenge: TEI and OntoLex deal with corpus citations in lexicons.
Approach: They argue that TEI and OntoLex can be used to model corpus citations in lexicons . they also argue that they should be combined to achieve a more accurate encoding .
Outcome: The proposed approach favours a combination of TEI and OntoLex . the proposed approach is based on a model of an example entry from a legacy dictionary .

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