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OFrLex: A Computational Morphological and Syntactic Lexicon for Old French
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-02677957 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. 3217-3225 (updated version) (2020)
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When Collaborative Treebank Curation Meets Graph Grammars ; When Collaborative Treebank Curation Meets Graph Grammars: Arborator With a Grew Back-End
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In: LREC 2020 - 12th Language Resources and Evaluation Conference ; https://hal.inria.fr/hal-03021720 ; LREC 2020 - 12th Language Resources and Evaluation Conference, May 2020, Marseille, France ; http://www.lrec-conf.org/proceedings/lrec2020/index.html (2020)
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On the Use of Dependencies in Relation Classification of Text with Deep Learning
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In: 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing2019) ; https://hal.archives-ouvertes.fr/hal-02103919 ; 20th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing2019), Apr 2019, La Rochelle, France (2019)
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Automatic and Adaptative Emojis Recommendation ; Recommandation automatique et adaptative d'emojis
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In: https://hal-amu.archives-ouvertes.fr/tel-02491135 ; Informatique et langage [cs.CL]. Aix-Marseille Université, 2019. Français (2019)
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Multilingual Fake News Detection with Satire
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In: CICLing: International Conference on Computational Linguistics and Intelligent Text Processing ; https://halshs.archives-ouvertes.fr/halshs-02391141 ; CICLing: International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2019, La Rochelle, France (2019)
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LIS at SemEval-2018 Task 2: Mixing Word Embeddings and Bag of Features for Multilingual Emoji Prediction
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In: SemEVAL ; https://hal-amu.archives-ouvertes.fr/hal-01871338 ; SemEVAL, Jun 2018, New-Orleans, United States (2018)
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From Emoji Usage to Categorical Emoji Prediction
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In: 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2018) ; https://hal-amu.archives-ouvertes.fr/hal-01871045 ; 19th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2018), Mar 2018, Hanoï, Vietnam ; https://www.cicling.org/2018/ (2018)
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From Emojis to Sentiment Analysis
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In: WACAI 2016 ; https://hal-amu.archives-ouvertes.fr/hal-01529708 ; WACAI 2016, Lab-STICC; ENIB; LITIS, Jun 2016, Brest, France ; http://www.enib.fr/wacai/ (2016)
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Analyse syntaxique de l'ancien français : quelles propriétés de la langue influent le plus sur la qualité de l'apprentissage ?
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In: TALN 22 ; https://hal.archives-ouvertes.fr/hal-01251006 ; TALN 22, Jun 2015, Caen, France. ; https://taln2015.greyc.fr/articlesenlignetaln/ (2015)
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Searching for Discriminative Metadata of Heterogenous Corpora
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In: Fourteenth International Workshop on Treebanks and Linguistic Theories (TLT14) ; https://hal.archives-ouvertes.fr/hal-01250981 ; Fourteenth International Workshop on Treebanks and Linguistic Theories (TLT14), Dec 2015, Varsovie, Poland. pp.72-82 ; http://tlt14.ipipan.waw.pl/proceedings/ (2015)
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Parsing Poorly Standardized Language Dependency on Old French
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In: Thirteenth International Workshop on Treebanks and Linguistic Theories (TLT13) ; https://hal.archives-ouvertes.fr/hal-01250959 ; Thirteenth International Workshop on Treebanks and Linguistic Theories (TLT13), Dec 2014, Tübingen, Germany. pp.51-61 ; http://tlt13.sfs.uni-tuebingen.de/ (2014)
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Abstract:
International audience ; This paper presents results of dependency parsing of Old French, a language which is poorly standardized at the lexical level, and which displays a relatively free word order. The work is carried out on five distinct sample texts extracted from the dependency treebank Syntactic Reference Corpus of Medieval French (SRCMF). Following Achim Stein's previous work, we have trained the Mate parser on each sub-corpus and cross-validated the results. We show that the parsing efficiency is diminished by the greater lexical variation of Old French compared to parse results on modern French. In order to improve the result of the POS tagging step in the parsing process, we applied a pre-treatment to the data, comparing two distinct strategies: one using a slightly post-treated version of the TreeTagger trained on Old French by Stein, and a CRF trained on the texts, enriched with external resources. The CRF version outperforms every other approach.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; corpus exploration; dependency parsing; machine learning; Old French; POS labelling
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URL: https://hal.archives-ouvertes.fr/hal-01250959
https://hal.archives-ouvertes.fr/hal-01250959v2/document
https://hal.archives-ouvertes.fr/hal-01250959v2/file/guibon_al_TLT2014.pdf
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