Identifying Opinion Holders for Question Answering in Opinion Texts

Soo-Min Kim and Eduard Hovy

Question answering in opinion texts has so far mostly concentrated on the identification of opinions and on analyzing the sentiment expressed in opinions. In this paper, we address another important part of Question Answering (QA) in opinion texts: finding opinion holders. Holder identification is a central part of full opinion identification and can be used independently to answer several opinion questions such as “Is China supporting Bush’s war on Iraq?” and “Do Iraqi people want U.S. troops in their soil?” Our system automatically learns the syntactic features signaling opinion holders using a Maximum Entropy ranking algorithm trained on human annotated data. Using syntactic parsing features, our system achieved 64% accuracy on identifying the holder of opinions in the MPQA dataset.

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