By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Information Technology. According to news reporting from North York, Canada, by VerticalNews journalists, research stated, "Traditionally, in many probabilistic retrieval models, query terms are assumed to be independent. Although such models can achieve reasonably good performance, associations can exist among terms from a human being's point of view."
The news correspondents obtained a quote from the research from York University, "There are some recent studies that investigate how to model term associations/dependencies by proximity measures. However, the modeling of term associations theoretically under the probabilistic retrieval framework is still largely unexplored. In this article, we introduce a new concept cross term, to model term proximity, with the aim of boosting retrieval performance. With cross terms, the association of multiple query terms can be modeled in the same way as a simple unigram term. In particular, an occurrence of a query term is assumed to have an impact on its neighboring text. The degree of the query-term impact gradually weakens with increasing distance from the place of occurrence. We use shape functions to characterize such impacts. Based on this assumption, we first propose a bigram CRoss TErm Retrieval (CRTER2) model as the basis model, and then recursively propose a generalized n-gram CRoss TErm Retrieval (CRTERn) model for n query terms, where n> 2. Specifically, a bigram cross term occurs when the corresponding query terms appear close to each other, and its impact can be modeled by the intersection of the respective shape functions of the query terms. For an n-gram cross term, we develop several distance metrics with different properties and employ them in the proposed models for ranking. We also show how to extend the language model using the newly proposed cross terms."
According to the news reporters, the research concluded: "Extensive experiments on a number of TREC collections demonstrate the effectiveness of our proposed models."
For more information on this research see: Modeling Term Associations for Probabilistic Information Retrieval. ACM Transactions on Information Systems, 2014;32(2):27-73. ACM Transactions on Information Systems can be contacted at: Assoc Computing Machinery, 2 Penn Plaza, Ste 701, New York, NY 10121-0701, USA.
Our news journalists report that additional information may be obtained by contacting J.S. Zhao, York University, Informat Retrieval & Knowledge Management Res Lab, Sch Informat Technol, North York, ON M3J 1P3, Canada. Additional authors for this research include J.X. Huang and Z. Ye.
Keywords for this news article include: Canada, Ontario, North York, Information Technology, North and Central America
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