Abstract
With the development of the Internet, network information is booming, and a large amount of short text data has brought more timely and comprehensive information to people. How to find the required information quickly and accurately from these pieces of information is the focus of the industry. Short text processing is one of the key technologies. Because of the sparse and noisy features of short texts, the traditional classification method can not provide good support. At present, the research on short text classification mainly focuses on two aspects: feature processing and classification algorithm. Most feature processing methods only use text literal information when performing feature expansion, which lacks the ability to discriminate the polysemy that is common in Chinese. In the classification algorithm, there are also problems such as insufficient input characteristics and insufficient classification effect. In order to improve the accuracy of Chinese short text classification, this paper proposes a method of Chinese short text classification based on improved convolutional recurrent neural network and concept graph, which achieves better classification results than existing algorithms.
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Acknowledgment
This work was partially supported by a grant from the NSF (Natural Science Foundation) of China under grant number 61673304 and U1836118, the Key Projects of National Social Science Foundation of China under grant number 11&ZD189.
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Liao, J., Sun, F., Gu, J. (2020). Combining Concept Graph with Improved Neural Networks for Chinese Short Text Classification. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_20
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DOI: https://doi.org/10.1007/978-981-15-3412-6_20
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