Opinion Extraction Using a Learning-Based Anaphora Resolution Technique
Nozomi Kobayashi, Ryu Iida, Kentaro Inui, Yūji Matsumoto · 2005
This paper addresses the task of extract-ing opinions from a given document collection. Assuming that an opinion can be represented as a tuple 〈Subject, Attribute, Value〉, we propose a compu-tational method to extract such tuples from texts. In this method, the main task is decomposed into (a) the pro-cess of extracting Attribute-Value pairs from a given text and (b) the process of judging whether an extracted pair ex-presses an opinion of the author. We apply machine-learning techniques to both subtasks. We also report on the results of our experiments and discuss future directions. 1