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Extracting hypernym relations from Wikipedia disambiguation pages: comparing symbolic and machine learning approaches
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In: Proceedings of IWCMC 2017 ; International Conference on Computational Semantics (IWCS 2017) ; https://hal.archives-ouvertes.fr/hal-02355277 ; International Conference on Computational Semantics (IWCS 2017), Sep 2017, Montpellier, France. pp.1-12 (2017)
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A Distant Learning Approach for Extracting Hypernym Relations from Wikipedia Disambiguation Pages
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In: Procedia Computer Science - Vol. 112 - 2017 ; 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2017) ; https://hal.archives-ouvertes.fr/hal-01919073 ; 21st International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2017), Sep 2017, Marseille, France. pp.1764-1773 (2017)
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Extraction de relations : combiner les techniques pour s'adapter à la diversité du texte.
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In: 28es Journées francophones d'Ingénierie des Connaissances IC 2017 ; https://hal.archives-ouvertes.fr/hal-01570070 ; 28es Journées francophones d'Ingénierie des Connaissances IC 2017, AFIA, Jul 2017, Caen, France. pp.86-97 ; https://pfia2017.greyc.fr/ic/presentation (2017)
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LITL at CLEF eHealth2017: automatic classication of death reports
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Ho-Dac, Lydia-Mai; Fabre, Cécile; Birski, Anouk; Boudraa, Imane; Bourriot, Aline; Cassier, Manon; Delvenne, Léa; Garcia-Gonzalez, Charline; Kang, Eun-Bee; Piccinini, Elisa; Rohrbacher, Camille; Séguier, Aure
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In: CLEF eHealth 2017 ; https://hal.archives-ouvertes.fr/hal-01702705 ; CLEF eHealth 2017, Sep 2017, Dublin, Ireland (2017)
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Abstract:
International audience ; This paper describes the participation of a group of students supervised by two teachers to the CLEF eHealth 2017 campaign, task 1. The task involves the classication of death certicates in French and more precisely the labelling of each cause of death with the relevant ICD10 code. The system that performs the automatic coding is based on an information retrieval method using the Solr interface. Two runs were submitted according to whether the system distinguishes cases of multiple causes or not. The best performance was obtained with the system which distinguishes multiple causes, with a precision of 0.61 and a recall of 0.55.
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Keyword:
[SHS.LANGUE]Humanities and Social Sciences/Linguistics; biomedical texts; cause of death extraction; code assignment; information retrieval; Text classication
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URL: https://hal.archives-ouvertes.fr/hal-01702705/file/LITL_paper_60.pdf
https://hal.archives-ouvertes.fr/hal-01702705/document
https://hal.archives-ouvertes.fr/hal-01702705
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