Challenges in information retrieval and language modeling

Statistical language modeling for information retrieval

Language Modeling Approaches to Information Retrieval

This article surveys recent research in the area of language modeling (sometimes called statistical language modeling) approaches to information retrieval. Language modeling is a formal probabilistic retrieval framework with roots in speech recognition and natural language processing. The underlying assumption of language modeling is that human language generation is a random process; the goal ...

Structured queries, language modeling, and relevance modeling in cross-language information retrieval

Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in a...

Word Pairs in Language Modeling for Information Retrieval

Previous language modeling approaches to information retrieval have focused primarily on single terms. The use of bigram models has been studied, but the restriction on word order and adjacency may not be justified for information retrieval. We propose a new language modeling approach to information retrieval that incorporates lexical affinities, or pairs of words that occur near each other, wi...

Information Retrieval Challenges in Web

Information Retrieval Challenges in Web G.N.K.Suresh Babu S.K.Saravanan and Dr.S.K.Srivatsa Associate Professor, Department of Computer Applications, GKMCET, Chennai. Assistant Professor [SG], Department of Computer Applications, Valliammai Engineering College, Chennai, INDIA Sr.Professor – St.Joseph‘s College of Engineering, Chennai, INDIA. _____________________________________________________...