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Towards a Cleaner Document-Oriented Multilingual Crawled Corpus
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In: https://hal.inria.fr/hal-03536361 ; 2022 (2022)
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Ungoliant: An Optimized Pipeline for the Generation of a Very Large-Scale Multilingual Web Corpus
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In: CMLC 2021 - 9th Workshop on Challenges in the Management of Large Corpora ; https://hal.inria.fr/hal-03301590 ; CMLC 2021 - 9th Workshop on Challenges in the Management of Large Corpora, Jul 2021, Limerick / Virtual, Ireland. ⟨10.14618/ids-pub-10468⟩ ; https://www.cl2021.org/ (2021)
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Establishing a New State-of-the-Art for French Named Entity Recognition
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02617950 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org (2020)
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CamemBERT: a Tasty French Language Model
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In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889805 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. ⟨10.18653/v1/2020.acl-main.645⟩ (2020)
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A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages
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In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02863875 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, United States. ⟨10.18653/v1/2020.acl-main.156⟩ ; https://acl2020.org (2020)
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Abstract:
International audience ; We use the multilingual OSCAR corpus, extracted from Common Crawl via language classification, filtering and cleaning, to train monolingual contextualized word embeddings (ELMo) for five mid-resource languages. We then compare the performance of OSCAR-based and Wikipedia-based ELMo embeddings for these languages on the part-of-speech tagging and parsing tasks. We show that, despite the noise in the Common-Crawl-based OSCAR data, embeddings trained on OSCAR perform much better than monolingual embeddings trained on Wikipedia. They actually equal or improve the current state of the art in tagging and parsing for all five languages. In particular, they also improve over multilingual Wikipedia-based contextual embeddings (multilingual BERT), which almost always constitutes the previous state of the art, thereby showing that the benefit of a larger, more diverse corpus surpasses the cross-lingual benefit of multilingual embedding architectures.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]
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URL: https://hal.inria.fr/hal-02863875v2/file/ELMos.pdf
https://hal.inria.fr/hal-02863875v2/document
https://doi.org/10.18653/v1/2020.acl-main.156
https://hal.inria.fr/hal-02863875
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How OCR Performance can Impact on the Automatic Extraction of Dictionary Content Structures
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In: 19th annual Conference and Members’ Meeting of the Text Encoding Initiative Consortium (TEI) -What is text, really? TEI and beyond ; https://hal.archives-ouvertes.fr/hal-02263276 ; 19th annual Conference and Members’ Meeting of the Text Encoding Initiative Consortium (TEI) -What is text, really? TEI and beyond, Sep 2019, Graz, Austria (2019)
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Asynchronous Pipeline for Processing Huge Corpora on Medium to Low Resource Infrastructures
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In: 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7) ; https://hal.inria.fr/hal-02148693 ; 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7), Jul 2019, Cardiff, United Kingdom. ⟨10.14618/IDS-PUB-9021⟩ (2019)
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| 8 |
CamemBERT: a Tasty French Language Model
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In: https://hal.inria.fr/hal-02445946 ; 2019 (2019)
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Preparing the Dictionnaire Universel for Automatic Enrichment
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In: 10th International Conference on Historical Lexicography and Lexicology (ICHLL) ; https://hal.inria.fr/hal-02131598 ; 10th International Conference on Historical Lexicography and Lexicology (ICHLL), Jun 2019, Leeuwarden, Netherlands ; https://easychair.org/smart-program/ICHLL-10/ (2019)
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