Abstract
This paper proposes a verb-driven approach to extract 5W1H (Who, What, Whom, When, Where and How) event semantic information from Chinese online news. The main contributions of our work are two-fold: First, given the usual structure of a news story, we propose a novel algorithm to extract topic sentences by stressing the importance of news headline; Second, we extract event facts (i.e. 5W1H) from these topic sentences by applying a rule-based method (verb-driven) and a supervised machine-learning method (SVM). This method significantly improves the predicate-argument structure used in Automatic Content Extraction (ACE) Event Extraction (EE) task by considering valency (dominant capacity to noun phrases) of a Chinese verb. Extensive experiments on ACE 2005 datasets confirm its effectiveness and it also shows a very high scalability, since we only consider the topic sentences and surface text features. Based on this method, we build a prototype system named Chinese News Fact Extractor (CNFE). CNFE is evaluated on a real world corpus containing 30,000 newspaper documents. Experiment results show that CNFE can extract event facts efficiently.
This work is sponsored by Beijing Municipal Science & Technology Commission project “R & D of 3-dimensional risk warning and integrated prevention technology” and China Postdoctoral Science Foundation (No.20080440260).
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Wang, W., Zhao, D., Zou, L., Wang, D., Zheng, W. (2010). Extracting 5W1H Event Semantic Elements from Chinese Online News. In: Chen, L., Tang, C., Yang, J., Gao, Y. (eds) Web-Age Information Management. WAIM 2010. Lecture Notes in Computer Science, vol 6184. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14246-8_62
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DOI: https://doi.org/10.1007/978-3-642-14246-8_62
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