Learning to detect conversation focus of threaded discussions

  • Authors:
  • Donghui Feng;Erin Shaw;Jihie Kim;Eduard Hovy

  • Affiliations:
  • University of Southern California, Marina del Rey, CA;University of Southern California, Marina del Rey, CA;University of Southern California, Marina del Rey, CA;University of Southern California, Marina del Rey, CA

  • Venue:
  • HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
  • Year:
  • 2006

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Abstract

In this paper we present a novel feature-enriched approach that learns to detect the conversation focus of threaded discussions by combining NLP analysis and IR techniques. Using the graph-based algorithm HITS, we integrate different features such as lexical similarity, poster trustworthiness, and speech act analysis of human conversations with feature-oriented link generation functions. It is the first quantitative study to analyze human conversation focus in the context of online discussions that takes into account heterogeneous sources of evidence. Experimental results using a threaded discussion corpus from an undergraduate class show that it achieves significant performance improvements compared with the baseline system.