Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM

Senti-LSSVM: Sentiment-Oriented Multi-Relation Extraction with Latent Structural SVM Lizhen Qu Max Planck Institute for Informatics lqu@mpi-inf.mpg.de Yi Zhang Nuance Communications yi.zhang@nuance.com Rui Wang DFKI GmbH mars198356@hotmail.com Lili Jiang Max Planck Institute for Informatics ljiang@mpi-inf.mpg.de Rainer Gemulla Max Planck Institute for Informatics rgemulla@mpi-inf.mpg.de Gerhard Weikum Max Planck Institute for Informatics weikum@mpi-inf.mpg.de Abstract Extracting instances of sentiment-oriented re- lations from user-generated web documents is important for online marketing analysis. Un- like previous work, we formulate this extrac- tion 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 cor- pora that do not contain explicit annotations of sentiment-bearing expressions, and it can si- multaneously recognize instances of both bi- nary (polarity) and ternary (comparative) re- lations with regard to entity mentions of in- terest. 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. 1 Introduction Sentiment-oriented relation extraction (Choi et al., 2006) is concerned with recognizing sentiment po- larities and comparative relations between entities from natural language text. Identifying such rela- tions often requires syntactic and semantic analysis at both sentence and phrase level. Most prior work on sentiment analysis consider either i) subjective sentence detection (Yu and Kübler, 2011), ii) po- larity classification (Johansson and Moschitti, 2011; Wilson et al., 2005), or iii) comparative relation identification (Jindal and Liu, 2006; Ganapathib- hotla and Liu, 2008). In practice, however, differ- ent types of sentiment-oriented relations frequently coexist in documents. In particular, we found that more than 38% of the sentences in our test corpus contain more than one type of relations. The iso- lated analysis approach is inappropriate because i) it sacrifices acuracy by ignoring the intricate interplay among different types of relations; ii) it could lead to conflicting predictions such as estimating a relation candidate as both negative and comparative. There- fore, in this paper, we identify instances of both sen- timent polarities and comparative relations for enti- ties of interest simultaneously. We assume that all the mentions of entities and attributes are given, and entities are disambiguated. It is a widely used as- sumption when evaluating a module in a pipeline system that the outputs of preceding modules are error-free. To the best of our knowledge, the only exist- ing system capable of extracting both comparisons and sentiment polarities is a rule-based system pro- posed by Ding et al. (2009). We argue that it is better to tackle the task by using a unified model with structured outputs. It allows us to consider a set of correlated relation instances jointly and char- acterize their interaction through a set of soft and hard constraints. For example, we can encode con- straints to discourage an attribute to participate in a polarity relation and a comparative relation at the same time. As a result, the system …

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