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Discovering Fine-Grained Sentiment with Latent Variable Structured Prediction Models
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Languages, Department of Linguistics and Philology.
2011 (English)In: Advances in Information Retrieval: 33rd European Conference on IR Research, ECIR 2011, Dublin, Ireland, April 18-21, 2011. Proceedings / [ed] Paul Clough, Colum Foley, Cathal Gurrin, Gareth J. F. Jones, Wessel Kraaij, Hyowon Lee, Vanessa Mudoch, Springer Berlin/Heidelberg, 2011, 368-374 p.Conference paper (Refereed)
Abstract [en]

In this paper we investigate the use of latent variable structured prediction models for fine-grained sentiment analysis in the common situation where only coarse-grained supervision is available. Specifically. we show how sentence-level sentiment labels can be effectively learned from document-level supervision using hidden conditional random fields (HCRFs) [10]. Experiments show that this technique reduces sentence classification errors by 22% relative to using a lexicon and 13% relative to machine-learning baselines.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2011. 368-374 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 6611
National Category
Language Technology (Computational Linguistics)
Research subject
Computational Linguistics
URN: urn:nbn:se:uu:diva-173962DOI: 10.1007/978-3-642-20161-5_37ISI: 000301968000037ISBN: 978-3-642-20160-8ISBN: 978-3-642-20161-5 (online)OAI: diva2:525879
33rd European Conference on Information Retrieval, APR 18-21, 2011, Dublin, IRELAND
Available from: 2013-04-10 Created: 2012-05-09 Last updated: 2013-04-10Bibliographically approved

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Täckström, Oscar
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ReferencesLink to record
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