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Detecting Opinionated Sentences by Extracting Context Information

Journal article published in 1 by Meng Xinfan, Wang Houfeng
This paper was not found in any repository; the policy of its publisher is unknown or unclear.
This paper was not found in any repository; the policy of its publisher is unknown or unclear.

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Abstract

In this paper, we briefly describe several experimental methods to solve MOAT at NTCIR-7. In the subtask of opinionated sentence detection, two methods aiming to extract the context information of each sentence are proposed. Maximum Entropy model is used to predict the polarity class. A rule-based pattern matching scheme is devised to find topic-relevant sentence. For the subtask of detecting holders and targets, the CRF model is adopted.