Chen K.-YLiu S.-HChen BJan E.-EWang H.-MHsu W.-LHSIN-HSI CHEN2023-06-092023-06-092014https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961345779&doi=10.3115%2fv1%2fd14-1156&partnerID=40&md5=9a206493249977fe5f43d4e36731cfb1https://scholars.lib.ntu.edu.tw/handle/123456789/632517Statistical language modeling (LM) that purports to quantify the acceptability of a given piece of text has long been an interesting yet challenging research area. In particular, language modeling for information retrieval (IR) has enjoyed remarkable empirical success; one emerging 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 continuation of such a general line of research and the main contribution is threefold. First, we propose a principled framework which can unify the relationships among several widely-used query modeling formulations. Second, on top of the successfully developed framework, we propose an extended query modeling formulation by incorporating critical query- specific information cues to guide the model estimation. Third, we further adopt and formalize such a framework to the speech recognition and summarization tasks. A series of empirical experiments reveal the feasibility of such an LM framework and the performance merits of the deduced models on these two tasks. © 2014 Association for Computational Linguistics.Computational linguistics; Computer hardware description languages; Modeling languages; Natural language processing systems; Empirical experiments; Language model; Model estimation; Pseudo relevance feedback; Query model; Retrieval effectiveness; Specific information; Statistical language modeling; Speech recognitionconference paper10.3115/v1/d14-11562-s2.0-84961345779