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Creation of a multilingual aligned corpus with Ukrainian as the target language and its exploitation
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In: Computational Linguistics and Intelligent Systems ; https://hal.archives-ouvertes.fr/hal-01736363 ; Computational Linguistics and Intelligent Systems, Apr 2017, Kharkiv, Ukraine (2017)
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Unsupervised acquisition of morphological resources for Ukrainian
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In: Computational Linguistics and Intelligent Systems ; https://hal.archives-ouvertes.fr/hal-01736400 ; Computational Linguistics and Intelligent Systems, Apr 2017, Kharkiv, Ukraine (2017)
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Understanding of unknown medical words
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In: Biomedical NLP Workshop associated with RANLP 2017 ; https://hal.archives-ouvertes.fr/hal-01736408 ; Biomedical NLP Workshop associated with RANLP 2017, Sep 2017, Varna, Bulgaria (2017)
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Querying biomedical Linked Data with natural language questions
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In: ISSN: 1570-0844 ; EISSN: 2210-4968 ; Semantic Web – Interoperability, Usability, Applicability ; https://hal.archives-ouvertes.fr/hal-01426686 ; Semantic Web – Interoperability, Usability, Applicability, IOS Press, 2017, 8, pp.581-599. ⟨10.3233/SW-160244⟩ (2017)
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Generating and executing complex natural language queries across linked data
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In: International Congress on Medical Informatics ; https://hal.archives-ouvertes.fr/hal-01971222 ; International Congress on Medical Informatics, Jan 2015, Sao Paulo, Brazil (2015)
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Tuning HeidelTime for identifying time expressions in clinical texts in English and French
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In: International Workshop on Health Text Mining and Information Analysis ; https://hal.archives-ouvertes.fr/hal-01972761 ; International Workshop on Health Text Mining and Information Analysis, Jan 2014, Gothenburg, Sweden (2014)
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Combining an expert-based medical entity recognizer to a machine-learning system: methods and a case-study
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In: Biomedical Informatics Insights ; https://hal.archives-ouvertes.fr/hal-01972779 ; Biomedical Informatics Insights, 2013, 13p (2013)
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Abstract:
International audience ; Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system, for instance in the form of lexicons and pattern-based rules, are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. We examine different methods to combine two such systems and test the most relevant ones through experiments performed on the i2b2/VA 2012 challenge data. Our case study specifically highlights the risk of overfitting incurred by an expert-based system. We observe that it prevents the combination of the two systems from obtaining improvements in precision, recall, or F-measure, and analyse the underlying mechanisms through a post-hoc feature-level analysis. We also observe that wrapping the expert-based system alone as attributes input to a CRF classifier does boost its F-measure from 0.603 to 0.710 (strict matching of types and boundaries, as per the conlleval program), bringing it on par with the data-driven system. The generality of this method remains to be further investigated.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs]; Hybrid Meth- ods; Information Extraction; Machine Learning; Medical records; Natural Language Processing; Overfitting
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URL: https://hal.archives-ouvertes.fr/hal-01972779
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