Paper: Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization

ACL ID D14-1156
Title Leveraging Effective Query Modeling Techniques for Speech Recognition and Summarization
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

Statistical language modeling (LM) that purports to quantify the acceptability of a given piece of text has long been an in- teresting yet challenging research area. In particular, language modeling for infor- mation retrieval (IR) has enjoyed re- markable empirical success; one emerg- ing stream of the LM approach for IR is to employ the pseudo-relevance feedback process to enhance the representation of an input query so as to improve retrieval effectiveness. This paper presents a con- tinuation of such a general line of re- search and the main contribution is three- fold. First, we propose a principled framework which can unify the relation- ships among several widely-used query modeling formulations. Second, on top of the successfully developed framework, we propose an extend...