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  A Weakly Supervised Model for Sentence-level Semantic Orientation Analysis with Multiple Experts

Qu, L., Gemulla, R., & Weikum, G. (2012). A Weakly Supervised Model for Sentence-level Semantic Orientation Analysis with Multiple Experts. In 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 149-159). Stroudsburg, PA: ACL.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0014-5FAF-0 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0023-C15B-A
Genre: Konferenzbeitrag

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MEM_EMNLP_camera_ready.pdf (beliebiger Volltext), 309KB
 
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 Urheber:
Qu, Lizhen1, 2, Autor           
Gemulla, Rainer1, Autor           
Weikum, Gerhard1, Autor           
Affiliations:
1Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              
2International Max Planck Research School, MPI for Informatics, Max Planck Society, ou_1116551              

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Schlagwörter: -
 Zusammenfassung: We propose the weakly supervised \emph{Multi-Experts Model} (MEM) for analyzing the semantic orientation of opinions expressed in natural language reviews. In contrast to most prior work, MEM predicts both opinion polarity and opinion strength at the level of individual sentences; such fine-grained analysis helps to understand better why users like or dislike the entity under review. A key challenge in this setting is that it is hard to obtain sentence-level training data for both polarity and strength. For this reason, MEM is weakly supervised: It starts with potentially noisy indicators obtained from coarse-grained training data (i.e., document-level ratings), a small set of diverse base predictors, and, if available, small amounts of fine-grained training data. We integrate these noisy indicators into a unified probabilistic framework using ideas from ensemble learning and graph-based semi-supervised learning. Our experiments indicate that MEM outperforms state-of-the-art methods by a significant margin.

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Sprache(n): eng - English
 Datum: 2012
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: -
 Identifikatoren: eDoc: 647494
URI: http://aclweb.org/anthology-new/D/D12/D12-1014.pdf
Anderer: Local-ID: C1256DBF005F876D-75AE874C4A8E5F21C1257B0800733FFD-Qu2012a
BibTex Citekey: Qu2012a
 Art des Abschluß: -

Veranstaltung

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Titel: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Veranstaltungsort: Jeju Island, Korea
Start-/Enddatum: 2012-07-12 - 2012-07-14

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Titel: 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
  Kurztitel : EMNLP-CoNLL 2012
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Stroudsburg, PA : ACL
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 149 - 159 Identifikator: ISBN: 978-1-937284-43-5