Biased LexRank: Passage retrieval using random walks with question-based priors

  • Authors:

  • Jahna Otterbacher;Gunes Erkan;Dragomir R. Radev

  • Affiliations:

  • School of Information, University of Michigan, Ann Arbor, MI 48109-1092, United States;Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109-1092, United States;School of Information, University of Michigan, Ann Arbor, MI 48109-1092, United States and Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109-1092, United States

  • Venue:
  • Information Processing and Management: an International Journal
  • Year:
  • 2009

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Abstract

We present Biased LexRank, a method for semi-supervised passage retrieval in the context of question answering. We represent a text as a graph of passages linked based on their pairwise lexical similarity. We use traditional passage retrieval techniques to identify passages that are likely to be relevant to a user's natural language question. We then perform a random walk on the lexical similarity graph in order to recursively retrieve additional passages that are similar to other relevant passages. We present results on several benchmarks that show the applicability of our work to question answering and topic-focused text summarization.