Answer models for question answering passage retrieval

Andrés Corrada-Emmanuel, W. Bruce Croft

Abstract: Answer patterns have been shown to improve the perfor-mance of open-domain factoid QA systems. Their use, however, requires either constructing the patterns manually or developing algorithms for learning them automatically. We present here a simpler approach that extends the techniques of language modeling to create answer models. These are language models trained on the correct answers to training questions. We show how they fit naturally into a probabilis-tic model for answer passage retrieval and demonstrate their effectiveness on the TREC 2002 QA Corpus.