Weighted-LDA-TVM: Using a Weighted Topic Vector Model for Measuring Short Text Similarity
Topic modeling is the core task of the similarity measurement of short texts and is widely used in the fields of information retrieval and sentiment analysis. Though latent dirichlet allocation provides an approach to model texts by mining the underlying semantic themes of texts. It often leads to a low accuracy of text similarity calculation because of the feature sparseness and poor topic focus of short texts. This paper proposes a similarity measurement method of short texts based on a new topic model, namely Weighted-LDA-TVM. Latent dirichlet allocation is adopted to capture the latent topics of short texts. The topic weights are learned by using particle swarm optimization. Finally, a text vector can be constructed based on the word embeddings of weighted topics for measuring the similarity of short texts. A group of text similarity measurement experiments were performed on biomedical literature abstracts about antidepressant drugs. The experimental results prove that the proposed model has the better distinguish ability and semantic representation ability for the similarity measurement of short texts.
KeywordsSimilarity measurement of short texts Topic model LDA PSO Word embedding
The work is supported by Science and Technology Project of Beijing Municipal Commission of Education (No. KM201710005026), National Basic Research Program of China (No. 2014CB744600), Beijing Key Laboratory of MRI and Brain Informatics.
- 1.Theobald, M., Siddharth, J., Paepcke, A.: Spotsigs: robust and efficient near duplicate detection in large web collections. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 563–570. ACM (2008)Google Scholar
- 2.Kumar, N.: Approximate string matching algorithm. Int. J. Comput. Sci. Eng. 2(3), 641–644 (2010)Google Scholar
- 3.Mohtarami, M., Lan, M., et al.: Sense sentiment similarity: an analysis. In: Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 1706–1712. AAAI Press (2012)Google Scholar
- 4.Kenter, T., Rijke, M.D.: Short text similarity with word embeddings. In: ACM International on Conference on Information and Knowledge Management, pp. 1411–1420. ACM (2015)Google Scholar
- 5.Kusner, M.J., Sun, Y., Kolkin, N.I., et al.: From word embeddings to document distances. In: International Conference on International Conference on Machine Learning, pp. 957–966. JMLR.org (2015)Google Scholar
- 6.Song, Y., Dan, R.: Unsupervised sparse vector densification for short text similarity. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1275–1280 (2015)Google Scholar
- 8.Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents, vol. 4, p. II-1188 (2014)Google Scholar
- 9.Boom, C.D., Canneyt, S.V., Bohez, S., et al.: Learning semantic similarity for very short texts, pp. 1229–1234 (2015)Google Scholar
- 10.Bafna, P., Pramod, D., Vaidya, A.: Document clustering: TF-IDF approach. In: International Conference on Electrical, Electronics, and Optimization Techniques (2016)Google Scholar
- 11.Huo, Z., Wu, J., Lu, Y., Li, C.: A topic-based cross-language retrieval model with PLSA and TF-IDF. In: International Conference on Big Data Analysis, pp. 340–344 (2018)Google Scholar