Effective document labeling with very few seed words: A topic modeling approach

Chenliang Li*, Jian Xing, Aixin Sun, Zongyang Ma

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

53 Citations (Scopus)

Abstract

Developing text classifiers often requires a large number of labeled documents as training examples. However, manually labeling documents is costly and time-consuming. Recently, a few methods have been proposed to label documents by using a small set of relevant keywords for each category, known as dataless text classification. In this paper, we propose a Seed-Guided Topic Model (named STM) for the dataless text classification task. Given a collection of unla-beled documents, and for each category a small set of seed words that are relevant to the semantic meaning of the category, the STM predicts the category labels of the documents through topic influence. STM models two kinds of topics: category-topics and general-topics. Each category-topic is associated with one specific category, representing its semantic meaning. The general-topics capture the global semantic information underlying the whole document collection. STM assumes that each document is associated with a single category-topic and a mixture of general-topics. A novelty of the model is that STM learns the topics by exploiting the explicit word co-occurrence patterns between the seed words and regular words (i.e., non-seed words) in the document collection. A document is then labeled, or classified, based on its posterior category-topic assignment. Experiments on two widely used datasets show that STM consistently outperforms the state-of-the-art dataless text classifiers. In some tasks, STM can also achieve comparable or even better classification accuracy than the state-of-the-art supervised learning solutions. Our experimental results further show that STM is insensitive to the tuning parameters. Stable performance with little variation can be achieved in a broad range of parameter settings, making it a desired choice for real applications.

Original languageEnglish
Title of host publicationCIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages85-94
Number of pages10
ISBN (Electronic)9781450340731
DOIs
Publication statusPublished - 24 Oct 2016
Externally publishedYes
Event25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States
Duration: 24 Oct 201628 Oct 2016

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume24-28-October-2016

Conference

Conference25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country/TerritoryUnited States
CityIndianapolis
Period24/10/1628/10/16

Keywords

  • Dataless text classification
  • Text analysis
  • Topic modeling

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