Learning to Detect Conversation Focus of Threaded Discussions

Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, pages 208–215, New York, June 2006. c⃝2006 Association for Computational Linguistics Learning to Detect Conversation Focus of Threaded Discussions Donghui Feng Erin Shaw Jihie Kim Eduard Hovy Information Sciences Institute University of Southern California Marina del Rey, CA, 90292 {donghui, shaw, jihie, hovy}@isi.edu Abstract In this paper we present a novel feature- enriched approach that learns to detect the conversation focus of threaded discus- sions by combining NLP analysis and IR techniques. Using the graph-based algo- rithm HITS, we integrate different fea- tures 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 hu- man conversation focus in the context of online discussions that takes into account heterogeneous sources of evidence. Ex- perimental results using a threaded dis- cussion corpus from an undergraduate class show that it achieves significant per- formance improvements compared with the baseline system. 1 Introduction Threaded discussion is popular in virtual cyber communities and has applications in areas such as customer support, community development, inter- active reporting (blogging) and education. Discus- sion threads can be considered a special case of human conversation, and since we have huge re- positories of such discussion, automatic and/or semi-automatic analysis would greatly improve the navigation and processing of the information. A discussion thread consists of a set of messages arranged in chronological order. One of the main challenges in the Question Answering domain is how to extract the most informative or important message in the sequence for the purpose of answer- ing the initial question, which we refer to as the conversation focus in this paper. For example, people may repeatedly discuss similar questions in a discussion forum and so it is highly desirable to detect previous conversation focuses in order to automatically answer queries (Feng et al., 2006). Human conversation focus is a hard NLP (Natu- ral Language Processing) problem in general be- cause people may frequently switch topics in a real conversation. The threaded discussions make the problem manageable because people typically fo- cus on a limited set of issues within a thread of a discussion. Current IR (Information Retrieval) techniques are based on keyword similarity meas- ures and do not consider some features that are important for analyzing threaded discussions. As a result, a typical IR system may return a ranked list of messages based on keyword queries even if, within the context of a discussion, this may not be useful or correct. Threaded discussion is a special case of human conversation, where people may express their ideas, elaborate arguments, and answer others’ questions; many of these aspects are unexplored by traditional IR techniques. First, messages in threaded discussions are not a flat document set, which is a common assumption for most IR sys- tems. Due to the flexibility and special characteris- tics involved in human conversations, messages within a thread are not necessarily of …

Content truncated to 500 words