Word or Phrase? Learning Which Unit to Stress for Information Retrieval

The use of phrases in retrieval models has been proven to be helpful in the literature, but no particular research addresses the problem of discriminating phrases that are likely to degrade the retrieval performance from the ones that do not. In this paper, we present a retrieval framework that utilizes both words and phrases flexibly, followed by a general learning-to-rank method for learning the potential contribution of a phrase in retrieval. We also present useful features that reflect the compositionality and discriminative power of a phrase and its constituent words for optimizing the weights of phrase use in phrase-based retrieval models. Experimental results on the TREC collections show that our proposed method is effective.

• Type: Journal
• Year: 2009
• Where: ACL
• Authors: Young-In Song, Jung-Tae Lee, Hae-Chang Rim
• Keywords: ACL 2009, Computational Linguistics, General Learning-to-rank Method, Phrase-based Retrieval Models, Retrieval Models