Abstract
With the popularity of the Internet, short texts become common in our daily life. Data like tweets and online Q&A pairs are quite valuable in application domains such as question retrieval and personalized recommendation. However, the sparsity problem of short text brings huge challenges for learning topics with conventional topic models. Recently, models like Biterm Topic Model and Word Network Topic Model alleviate the sparsity problem by modeling topics on biterms or pseudo documents. They are encouraged to put words with higher semantic similarity into the same topic by using word co-occurrence. However, there exist many semantically similar words which rarely co-occur. To address this limitation, we propose a model named SEREIN which exploits word embeddings to find more comprehensive semantic representations. Compared with existing models, we improve the performance of topic discovery significantly. Experiments on two open-source and real-world short text datasets also show the effectiveness of involving word embeddings.
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Acknowledgments
This paper is supported by the National Key Research and Development Program of China (Grant No. 2016YF- B1001102), the National Natural Science Foundation of China (Grant Nos. 61502227, 61876080), the Fundamental Research Funds for the Central Universities No.020214380040, the Collaborative Innovation Center of Novel Software Technology and Industrialization at Nanjing University. We also would like to thank machine learning repository of UCI [9] and Yahoo! Research for the datasets.
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Lu, Hy., Li, Y., Tang, C., Wang, Cj., Xie, Jy. (2018). Constructing Pseudo Documents with Semantic Similarity for Short Text Topic Discovery. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_39
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