Abstract
Short text matching is a key problem in natural language processing (NLP), which can be applied in journalism, military, and other fields. In this paper, we propose an optimized Chinese short text matching algorithm based on external knowledge (OTE). OTE can effectively eliminate semantic ambiguity in Chinese text by integrating the HowNet external knowledge base. We use SoftLexicon to optimize the word lattice graph to provide more comprehensive multi-granularity information and integrate the LaserTagger model and EDA for data augmentation. Experimental results show that OTE has an average accuracy improvement of 1.5% in three datasets compared with existing models.
Supported by the Ministry of Science and Technology of China (No. 2020AAA0105100).
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Ma, H., Ding, Z., Li, Z., Guo, H. (2022). OTE: An Optimized Chinese Short Text Matching Algorithm Based on External Knowledge. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13368. Springer, Cham. https://doi.org/10.1007/978-3-031-10983-6_2
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