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A Semi-supervised Hidden Markov Topic Model Based on Prior Knowledge

  • Sattar Seifollahi
  • Massimo Piccardi
  • Ehsan Zare Borzeshi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 845)

Abstract

A topic model is an unsupervised model to automatically discover the topics discussed in a collection of documents. Most of the existing topic models only use bag-of-words representations or single-word distributions and do not consider relations between words in the model. As a consequence, these models may generate topics which are not in good agreement with human-judged topic coherence. To mitigate this issue, we present a topic model which employs topically-related knowledge from prior topics and words’ co-occurrence/relations in the collection. To incorporate the prior knowledge, we leverage a two-staged semi-supervised Markov topic model. In the first stage, we estimate a transition matrix and a low-dimensional vocabulary for the final topic model. In the second stage, we produce the final topic model where the topic assignment is performed following a Markov chain process. Experiments on real text documents from a major compensation agency demonstrate improvements of both the PMI score measure and the topic coherence.

Keywords

Topic modelling Hidden Markov model Latent Dirichlet allocation Topic coherence 

Notes

Acknowledgement

This project was funded by the Capital Market Cooperative Research Centre in combination with the Transport Accident Commission of Victoria. Acknowledgements and thanks to industry supervisors David Attwood (Lead Research Partnerships) and Bernie Kruger (Data Science Lead). This research has received ethics approval from University of Technology Sydney (UTS HREC REF NO. ETH16-0968).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Sattar Seifollahi
    • 1
    • 2
  • Massimo Piccardi
    • 1
  • Ehsan Zare Borzeshi
    • 2
  1. 1.Faculty of Engineering and Information TechnologyUniversity of Technology SydneySydneyAustralia
  2. 2.Capital Markets Cooperative Research Centre (CMCRC)SydneyAustralia

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