Abstract
Negative and speculative expressions are popular in natural language. Recently, negation and speculation detection has become an important task in computational linguistics community. However, there are few related research on Chinese negation and speculation detection. In this paper, a supervised machine learning method with conditional random fields (CRFs) is proposed to detect negative and speculative information in scientific literature. This paper also evaluates the effectiveness of each feature under the character-based and word-based framework, as well as the combination of features. Experimental results show that the single-word feature and the part of speech feature are effective, and the combined features improve the performance furthest. Our Chinese negation and speculation detection system in sentence level achieves 94.70% and 87.10% of accuracy, respectively.
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Chen, Z., Zou, B., Zhu, Q., Li, P. (2013). Chinese Negation and Speculation Detection with Conditional Random Fields. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2013. Communications in Computer and Information Science, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41644-6_4
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DOI: https://doi.org/10.1007/978-3-642-41644-6_4
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