Abstract
Semantic matching is widely used in many natural language processing tasks. In this paper, we focus on the semantic matching between short texts and design a model to generate deep features, which describe the semantic relevance between short “text object”. Furthermore, we design a method to combine shallow features of short texts (i.e., LSI, VSM and some other handcraft features) with deep features of short texts (i.e., word embedding matching of short text). Finally, a ranking model (i.e., RankSVM) is used to make the final judgment. In order to evaluate our method, we implement our method on the task of matching posts and responses. Results of experiments show that our method achieves the state-of-the-art performance by using shallow features and deep features.
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Kang, L., Hu, B., Wu, X., Chen, Q., He, Y. (2014). A Short Texts Matching Method Using Shallow Features and Deep Features. In: Zong, C., Nie, JY., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2014. Communications in Computer and Information Science, vol 496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45924-9_14
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DOI: https://doi.org/10.1007/978-3-662-45924-9_14
Publisher Name: Springer, Berlin, Heidelberg
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