Paper: Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic

ACL ID D13-1097
Title Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Explanatory sentences are employed to clarify reasons, details, facts, and so on. High quality online product reviews usually include not only positive or negative opinions, but also a variety of explanations of why these opinions were given. These explanations can help readers get easily comprehensible informa- tion of the discussed products and aspect- s. Moreover, explanatory relations can also benefit sentiment analysis applications. In this work, we focus on the task of identi- fying subjective text segments and extracting their corresponding explanations from prod- uct reviews in discourse level. We propose a novel joint extraction method using first- order logic to model rich linguistic features and long distance constraints. Experimental results demonstrate the effectiveness of the...