Abstract
Sentence categorization is a task to classify sentences by their types, which is very useful for the analysis of many NLP applications. There exist grammar or syntactic rules to determine types of sentences. And keywords like negation word for negative sentences is an important feature. However, no all sentences have rules to classify. Besides, different types of sentences may contain the same keywords whose meaning may be changed by context. We address the first issue by proposing a hybrid model consisting of Decision Trees and Support Vector Machines. In addition, we design a new feature based on N-gram model. The results of the experiments conducted on the sentence categorization dataset in “Good Ideas of China” Competition 2015 show that (1) our model outperforms baseline methods and all online systems in this competition; (2) the effectiveness of our feature is higher than that of features frequently used in NLP.
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This work is supported by National Natural Science Foundation of China (project no. 61300137), and NEMODE Network Pilot Study: A Computational Taxonomy of Business Models of the Digital Economy, P55805.
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Chen, Z., Yang, K., Cai, Y., Huang, D., Leung, Hf. (2016). A Highly Effective Hybrid Model for Sentence Categorization. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_9
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DOI: https://doi.org/10.1007/978-3-319-32055-7_9
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