Abstract
Opinions always carry important information of texts, but comparative sentence is a common way to express opinions. We describe how to recognize comparative sentences from Chinese text documents by combining rule-based methods and statistical methods as well as analyze the performance of these methods. The method firstly normalizes the corpus and Chinese word segmentation, and then gets the broad extraction results by using comparative words, sentence structure templates and dependency relation analysis. Finally we take CSR, comparative words and statistical feature words as classification features of SVM to accurately identify comparative sentences in the broad extraction results. The experiments with COAE 2013’s test data show that our approach provides better performance than the baselines and most systems reported at CCIR 2013.
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Liu, Q., Huang, H., Zhang, C., Chen, Z., Chen, J. (2013). Chinese Comparative Sentence Identification Based on the Combination of Rules and Statistics. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_27
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DOI: https://doi.org/10.1007/978-3-642-53917-6_27
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