Weighted-LDA-TVM: Using a Weighted Topic Vector Model for Measuring Short Text Similarity

  • Xiaobo He
  • Ning ZhongEmail author
  • Jianhui Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11976)


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.


Similarity 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.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Advanced Innovation Center for Future Internet TechnologyBeijing University of TechnologyBeijingChina
  3. 3.Beijing International Collaboration Base on Brain Informatics and Wisdom ServicesBeijingChina
  4. 4.Beijing Key Laboratory of MRI and Brain InformaticsBeijingChina
  5. 5.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan

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