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A dataset for automatic detection of places in (early) modern French texts ; Un jeu de données pour la détection automatique de lieux dans les textes français modernes
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In: NASSCFL 2021 - 50th Annual North American Society for Seventeenth-Century French Literature Conference ; https://hal.archives-ouvertes.fr/hal-03187097 ; NASSCFL 2021 - 50th Annual North American Society for Seventeenth-Century French Literature Conference, NASSCFL, May 2021, Iowa City / Virtual, United States. pp.5 (2021)
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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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Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets
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In: https://hal.inria.fr/hal-03177623 ; 2021 (2021)
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Expanding the content model of annotationBlock
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In: Next Gen TEI, 2021 - TEI Conference and Members’ Meeting ; https://hal.archives-ouvertes.fr/hal-03380805 ; Next Gen TEI, 2021 - TEI Conference and Members’ Meeting, Oct 2021, Virtual, United States (2021)
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Ungoliant: An optimized pipeline for the generation of a very large-scale multilingual web corpus ...
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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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Building a User-Generated Content North-African Arabizi Treebank: Tackling Hell
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In: ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.inria.fr/hal-02889804 ; ACL 2020 - 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Seattle / Virtual, Canada. ⟨10.18653/v1/2020.acl-main.107⟩ (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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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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| 11 |
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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| 13 |
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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| 14 |
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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| 15 |
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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Abstract:
International audience ; The Dictionnaire Universel (DU) is an encyclopaedic dictionary originally written by Antoine Furetière around 1676-78, later revised and improved by the Protestant jurist Henri Basnage de Beauval who expanded, corrected and included terms of arts, crafts and sciences, into the Dictionnaire.The aim of the BASNUM project is to digitize the DU in its second edition rewritten by Basnage de Beauval, to analyse it with computational methods in order to better assess the importance of this work for the evolution of sciences and mentalities in the 18th century, and to contribute to the contemporary movement for creating innovative and data-driven computational methods for text digitization, encoding and analysis.Based on the experience acquired within the research group, an enrichment workflow based upon a series of Natural Language Processing processes is being set up to be applied to Basnage's work. This includes, among others, automatic identification of the dictionary structure (macro-, meso- and microstructure), named-entity recognition (in particular persons and locations), classification of dictionary entries, detection and study of polysemy markers, tracking and classification of quotation use (bibliographic references), scoring semantic similarity between the DU and other dictionaries. The main challenges being the lack of available annotated data in order to train machine learning models, decreased accuracy when using modern pre-trained models due to the differences between present-day and 18th century French, and even unreliable or low quality OCRisation. The paper describes methods that are useful to tackle these issues in order to prepare the the DU for automatic enrichment going beyond what current available tools like Grobid-dictionaries can do, thanks to the advent of deep learning NLP models. The paper also describes how these methods could be applied to other dictionaries or even other types of ancient texts.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; [SHS.MUSEO]Humanities and Social Sciences/Cultural heritage and museology
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URL: https://hal.inria.fr/hal-02131598/document
https://hal.inria.fr/hal-02131598
https://hal.inria.fr/hal-02131598/file/ICHLL_10_Slides.pdf
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Asynchronous pipelines for processing huge corpora on medium to low resource infrastructures ...
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