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        <title>Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM</title>
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    <name type="personal">
        <namePart type="given">Lizhen</namePart>
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    <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.</abstract>
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%0 Journal Article
%T Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM
%A Qu, Lizhen
%A Zhang, Yi
%A Wang, Rui
%A Jiang, Lili
%A Gemulla, Rainer
%A Weikum, Gerhard
%J Transactions of the Association for Computational Linguistics
%D 2014
%V 2
%I MIT Press
%C Cambridge, MA
%F qu-etal-2014-senti
%X 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.
%R 10.1162/tacl_a_00173
%U https://aclanthology.org/Q14-1013
%U https://doi.org/10.1162/tacl_a_00173
%P 155-168

Markdown (Informal)

[Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM](https://aclanthology.org/Q14-1013) (Qu et al., TACL 2014)

• Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM (Qu et al., TACL 2014)

ACL

• Lizhen Qu, Yi Zhang, Rui Wang, Lili Jiang, Rainer Gemulla, and Gerhard Weikum. 2014. Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM. Transactions of the Association for Computational Linguistics, 2:155–168.