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Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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| 2 |
Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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The Vagueness of Vagueness in Noun Phrases
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In: International Conference on Automated Knowledge Base Construction (AKBC) ; https://hal-imt.archives-ouvertes.fr/hal-03344675 ; International Conference on Automated Knowledge Base Construction (AKBC), 2021, online, United States (2021)
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Platypus – A Multilingual Question Answering Platform for Wikidata
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In: https://hal.archives-ouvertes.fr/hal-01730479 ; [Technical Report] LIP - ENS Lyon. 2018 (2018)
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| 5 |
Demoing Platypus – A Multilingual Question Answering Platform for Wikidata
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In: ESWC 2018 - Extended Semantic Web Conference ; https://hal.archives-ouvertes.fr/hal-01824972 ; ESWC 2018 - Extended Semantic Web Conference, Jun 2018, Heracklion, Greece. pp.1-5, ⟨10.1007/978-3-319-98192-5_21⟩ (2018)
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Big Data Methods for Computational Linguistics
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In: ISSN: 1053-1238 ; Bulletin of the Technical Committee on Data Engineering ; https://hal.archives-ouvertes.fr/hal-01122699 ; Bulletin of the Technical Committee on Data Engineering, IEEE Computer Society, 2012, pp.10 (2012)
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Abstract:
International audience ; Many tasks in computational linguistics traditionally rely on hand-crafted or curated resources like the-sauri or word-sense-annotated corpora. The availability of big data, from the Web and other sources,has changed this situation. Harnessing these assets requires scalable methods for data and text ana-lytics. This paper gives an overview on our recent work that utilizes big data methods for enhancingsemantics-centric tasks dealing with natural language texts. We demonstrate a virtuous cycle in harvest-ing knowledge from large data and text collections and leveraging this knowledge in order to improvethe annotation and interpretation of language in Web pages and social media. Specifically, we show howto build large dictionaries of names and paraphrases for entities and relations, and how these help todisambiguate entity mentions in texts.
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Keyword:
[INFO]Computer Science [cs]
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URL: https://hal.archives-ouvertes.fr/hal-01122699
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