Passage Retrieval and Evaluation
MASSACHUSETTS UNIV AMHERST CENTER FOR INTELLIGENT INFORMATION RETRIEVAL
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Information retrieval researchers have studied passage retrieval extensively, yet there is no consensus within the community about how to evaluate the results of passage retrieval experiments. This paper describes five character-level passage evaluation measures and tasks for which they may be appropriate. In the second half of the paper we compare several passage retrieval models, including a new generative mixture model that outperforms strong baselines on many of the evaluation measures discussed in part one.
- Information Science
- Statistics and Probability