Predicting thread discourse structure over technical web forums

  • Authors:
  • Li Wang;Marco Lui;Su Nam Kim;Joakim Nivre;Timothy Baldwin

  • Affiliations:
  • University of Melbourne, and NICTA Victoria Research Laboratory;University of Melbourne, and NICTA Victoria Research Laboratory;University of Melbourne, and NICTA Victoria Research Laboratory;Uppsala University;University of Melbourne, and NICTA Victoria Research Laboratory

  • Venue:
  • EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2011

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Abstract

Online discussion forums are a valuable means for users to resolve specific information needs, both interactively for the participants and statically for users who search/browse over historical thread data. However, the complex structure of forum threads can make it difficult for users to extract relevant information. The discourse structure of web forum threads, in the form of labelled dependency relationships between posts, has the potential to greatly improve information access over web forum archives. In this paper, we present the task of parsing user forum threads to determine the labelled dependencies between posts. Three methods, including a dependency parsing approach, are proposed to jointly classify the links (relationships) between posts and the dialogue act (type) of each link. The proposed methods significantly surpass an informed baseline. We also experiment with "in situ" classification of evolving threads, and establish that our best methods are able to perform equivalently well over partial threads as complete threads.