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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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SinNer@Clef-Hipe2020 : Sinful adaptation of SotA models for Named Entity Recognition in French and German
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In: CLEF 2020 Working Notes. Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum ; https://hal.inria.fr/hal-02984746 ; CLEF 2020 Working Notes. Working Notes of CLEF 2020 - Conference and Labs of the Evaluation Forum, Sep 2020, Thessaloniki / Virtual, Greece ; https://impresso.github.io/CLEF-HIPE-2020/ (2020)
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French Contextualized Word-Embeddings with a sip of CaBeRnet: a New French Balanced Reference Corpus
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In: CMLC-8 - 8th Workshop on the Challenges in the Management of Large Corpora ; https://hal.inria.fr/hal-02678358 ; CMLC-8 - 8th Workshop on the Challenges in the Management of Large Corpora, May 2020, Marseille, France ; https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/CMLC-8book.pdf (2020)
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Abstract:
International audience ; This paper describes and compares the impact of different types and size of training corpora on language models like ELMO. By asking the fundamental question of quality versus quantity we evaluate four French corpora for training on parsing scores, POS-tagging and named-entities recognition downstream tasks. The paper studies the relevance of a new corpus, CaBeRnet, featuring a representative range of language usage, including a balanced variety of genres (oral transcriptions, newspapers, popular magazines, technical reports, fiction, academic texts), in oral and written styles. We hypothesize that a linguistically representative and balanced corpora will allow the language model to be more efficient and representative of a given language and therefore yield better evaluation scores on different evaluation sets and tasks.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; Balanced French Corpus; BERT; ELMo; French; Language Models; NER; Parsing; Tagging
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URL: https://hal.inria.fr/hal-02678358/file/LREC_Fabre_Ortiz.pdf
https://hal.inria.fr/hal-02678358
https://hal.inria.fr/hal-02678358/document
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