Abstract
In recent years, short text classification is attracting more attention. With the development of social platforms such as micro blogging and wechatting, Chinese short text classification has great impact on public opinion analysis and sentiment mining. Among social media texts, news headline classification has substantial influence on both academia and Internet economy. The issues such as semantic sparsity caused by the limited length of texts, and the grammatical nonstandard of the text, have prevented the performance of classification. In the paper, a Chinese news headline classification method based on multi model decision is proposed. First, an effective Convolutional Neural Network (CNN) is applied as one of text classifiers, at the same time, a Long Short-Term Memory (LSTM) is used as another text classifier as well. The aim is to obtain both abstract semantics of news headlines through CNN and context information between word sequences through LSTM. Second, an efficient text categorization tool - fastText (Facebook) is introduced to get the most excellent and balanced results. Finally, a decision model is proposed to favor the best performance of classification. A simple but very effective voting system is proposed and the result is very promising. Experiments based on the dataset from nlpcc 2017 Task2 has proved the efficiency of our method. Our method achieves much higher performance (\(F_{1}\) of 79%) than the baseline provided by nlpcc 2017.
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This work was supported by National Undergraduate Training Program for Innovation and Entrepreneurship(NO.201810635003)
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Cao, Y., Xu, X., Du, Y., He, J., Li, L. (2018). Hybrid Decision Based Chinese News Headline Classification. In: U, L., Xie, H. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 11268. Springer, Cham. https://doi.org/10.1007/978-3-030-01298-4_1
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DOI: https://doi.org/10.1007/978-3-030-01298-4_1
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