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Fuzzy Bag-of-Topics Model for Short Text Representation

  • Hao Jia
  • Qing Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11305)

Abstract

Text representation is the keystone in many NLP tasks. For short text representation learning, the traditional Bag-of-Words model (BoW) is often criticized for sparseness and neglecting semantic information. Fuzzy Bag-of-Words (FBoW) and Fuzzy Bag-of-Words Cluster (FBoWC) model are the improved model of BoW, which can learn dense and meaningful document vectors. However, word clusters in FBoWC model are obtained by K-means cluster algorithm, which is unstable and may result in incoherent word clusters if not initialized properly. In this paper, we propose the Fuzzy Bag-of-Topics model (FBoT) to learn short text vector. In FBoT model, word communities, which are more coherent than word clusters in FBoWC, are used as basis terms in text vector. Experimental results of short text classification on two datasets show that FBoT achieves the highest classification accuracies.

Keywords

Short text Representation learning Word communities 

Notes

Acknowledgements

This work was supported in part by Shanghai Innovation Action Plan Project under the grant No. 16511101200.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Computer Engineering and ScienceShanghai UniversityShanghaiChina

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