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Taxonomy-Augmented Features for Document Clustering

  • Sattar SeifollahiEmail author
  • Massimo Piccardi
  • Ehsan Zare Borzeshi
  • Bernie Kruger
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 996)

Abstract

In document clustering, individual documents are typically represented by feature vectors based on term-frequency or bag-of-word models. However, such feature vectors intrinsically dismiss the order of the words in the document and suffer from very high dimensionality. For these reasons, in this paper we present novel taxonomy-augmented features that enjoy two promising characteristics: (1) they leverage semantic word embeddings to take the word order into account, and (2) they reduce the feature dimensionality to a very manageable size. Our feature extraction approach consists of three main steps: first, we apply a word embedding technique to represent the words in a word embedding space. Second, we partition the word vocabulary into a hierarchy of clusters by using k-means hierarchically. Lastly, the individual documents are projected to the hierarchy and a compact feature vector is extracted. We propose two methods for generating the features: the first uses all the clusters in the hierarchy and results in a feature vector whose dimensionality is equal to the number of the clusters. The second uses a small set of user-defined words and results in an even smaller feature vector whose dimensionality is equal to the size of the set. Numerical experiments on document clustering show that the proposed approach is capable of achieving comparable or even higher accuracy than conventional feature vectors with a much more compact representation.

Notes

Acknowledgement

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

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.University of Technology SydneySydneyAustralia
  2. 2.Capital Markets Cooperative Research Centre (CMCRC)SydneyAustralia
  3. 3.Transport Accident Commission (TAC)GeelongAustralia

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