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

Qi Zhang, Jin Qian, Huan Chen, Jihua Kang, Xuanjing Huang

Abstract: 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 information of the discussed products and aspects. Moreover, explanatory relations can also benefit sentiment analysis applications. In this work, we focus on the task of identifying subjective text segments and extracting their corresponding explanations from product reviews in discourse level. We propose a novel joint extraction method using firstorder logic to model rich linguistic features and long distance constraints. Experimental results demonstrate the effectiveness of the proposed method.