Senti-LSSVM: sentiment-oriented multi-relation extraction with latent structural SVM

Lizhen Qu, Yi Zhang, Rui Wang, Lili Jiang, Rainer Gemulla, Gerhard Weikum

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Extracting instances of sentiment-oriented relations from user-generated web documents is important for online marketing analysis. Unlike previous work, we formulate this extraction task as a structured prediction problem and design the corresponding inference as an integer linear program. Our latent structural SVM based model can learn from training corpora that do not contain explicit annotations of sentiment-bearing expressions, and it can simultaneously recognize instances of both binary (polarity) and ternary (comparative) relations with regard to entity mentions of interest. The empirical evaluation shows that our approach significantly outperforms state-of-the-art systems across domains (cameras and movies) and across genres (reviews and forum posts). The gold standard corpus that we built will also be a valuable resource for the community.
Original languageEnglish
Pages (from-to)155–168
Number of pages14
JournalTransactions of the Association for Computational Linguistics
Volume2
DOIs
Publication statusPublished - 2014
Externally publishedYes

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