Papers by Gilles Sérasset
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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Michael Rosner, Sina Ahmadi, Elena-Simona Apostol, Julia Bosque-Gil, Christian Chiarcos, Milan Dojchinovski, Katerina Gkirtzou, Jorge Gracia, Dagmar Gromann, Chaya Liebeskind, Giedrė Valūnaitė Oleškevičienė, Gilles Sérasset, Ciprian-Octavian Truică
| 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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Dimitar Trajanov, Elena Apostol, Radovan Garabik, Katerina Gkirtzou, Dagmar Gromann, Chaya Liebeskind, Cosimo Palma, Michael Rosner, Alexia Sampri, Gilles Sérasset, Blerina Spahiu, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene
| 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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Dagmar Gromann, Hugo Goncalo Oliveira, Lucia Pitarch, Elena-Simona Apostol, Jordi Bernad, Eliot Bytyçi, Chiara Cantone, Sara Carvalho, Francesca Frontini, Radovan Garabik, Jorge Gracia, Letizia Granata, Fahad Khan, Timotej Knez, Penny Labropoulou, Chaya Liebeskind, Maria Pia Di Buono, Ana Ostroški Anić, Sigita Rackevičienė, Ricardo Rodrigues, Gilles Sérasset, Linas Selmistraitis, Mahammadou Sidibé, Purificação Silvano, Blerina Spahiu, Enriketa Sogutlu, Ranka Stanković, Ciprian-Octavian Truică, Giedre Valunaite Oleskeviciene, Slavko Zitnik, Katerina Zdravkova
| 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 . |