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1001 |
Teaching Computational Linguistics ; Challenges and Target Audiences
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Amaro, Raquel. - : COPEC - Science and Education Research Council, 2016
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1002 |
Automated Learning of Event Coding Dictionaries for Novel Domains with an Application to Cyberspace
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1004 |
On link predictions in complex networks with an application to ontologies and semantics
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Entrup, Bastian. - : Justus-Liebig-Universität Gießen, 2016. : FB 05 - Sprache, Literatur, Kultur. Germanistik, 2016
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1005 |
Ensembles of Text and Time-Series Models for Automatic Generation of Financial Trading Signals
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1006 |
Structured Approaches for Exploring Interpersonal Relationships in Natural Language Text
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1007 |
Extracting biomedical events from pairs of text entities
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In: ISSN: 1471-2105 ; BMC Bioinformatics ; https://hal.archives-ouvertes.fr/hal-01313324 ; BMC Bioinformatics, BioMed Central, 2015, 16 (Suppl 10), pp.S8. ⟨10.1186/1471-2105-16-S10-S8⟩ ; http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-16-S10-S8 (2015)
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1008 |
Sentiment Analysis with Incremental Human-in-the-Loop Learning and Lexical Resource Customization
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1009 |
Guided Probabilistic Topic Models for Agenda-setting and Framing
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1010 |
PREDICTING MUSIC GENRE PREFERENCES BASED ON ONLINE COMMENTS
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In: Master's Theses (2014)
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1011 |
Entity Information Extraction using Structured and Semi-structured resources ...
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1012 |
The USAGE review corpus for fine-grained, multi-lingual opinion analysis ...
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1013 |
The USAGE review corpus for fine-grained, multi-lingual opinion analysis ...
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1014 |
The USAGE review corpus for fine-grained, multi-lingual opinion analysis
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1015 |
Deep stochastic sentence generation : resources and strategies
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In: TDX (Tesis Doctorals en Xarxa) (2014)
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1016 |
Supervised and semi-supervised statistical models for word-based sentiment analysis ; Überwachte und halbüberwachte statistische Modelle zur wortbasierten Sentimentanalyse
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1017 |
Identification of Informativeness in Text using Natural Language Stylometry
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In: Electronic Thesis and Dissertation Repository (2014)
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1018 |
Analysing discourse and text complexity for learning and collaborating ; L'analyse de la complexité du discours et du texte pour apprendre et collaborer
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In: https://tel.archives-ouvertes.fr/tel-00978420 ; Education. Université de Grenoble; Universitatea politehnica (Bucarest), 2013. Français. ⟨NNT : 2013GRENH004⟩ (2013)
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1019 |
Pour une démarche centrée sur l'utilisateur dans les ENT. Apport au Traitement Automatique des Langues.
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In: https://tel.archives-ouvertes.fr/tel-01070522 ; Sciences de l'information et de la communication. Université de Caen, 2013 (2013)
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1020 |
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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