This study proposes a new way of using WordNet for query expansion (QE). We choose candidate expansion terms from a set of pseudo‐relevant documents; however, the usefulness of these terms is measured based on their definitions provided in a hand‐crafted lexical resource such as WordNet. Experiments with a number of standard TREC collections WordNet‐based that this method outperforms existing WordNet‐based methods. It also compares favorably with established QE methods such as KLD and RM3. Leveraging earlier work in which a combination of QE methods was found to outperform each individual method (as well as other well‐known QE methods), we next propose a combination‐based QE method that takes into account three different aspects of a candidate expansion term's usefulness: (a) its distribution in the pseudo‐relevant documents and in the target corpus, (b) its statistical association with query terms, and (c) its semantic relation with the query, as determined by the overlap between the WordNet definitions of the term and query terms. This combination of diverse sources of information appears to work well on a number of test collections, viz., TREC123, TREC5, TREC678, TREC robust (new), and TREC910 collections, and yields significant improvements over competing methods on most of these collections.

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This paper looks at the problem of searching for Indian language (IL) content on the Web. Even though the amount of IL content that is available on the Web is growing rapidly, searching through this content using the most popular websearch engines poses certain problems. Since the popular search engines do not use any stemming / orthographic normalization for Indian languages, recall levels for IL searches can be low. We provide some examples to indicate the extent of this problem, and suggest a simple and efficient solution to the problem.

The aim of the Forum for Information Retrieval Evaluation (FIRE) is to create an evaluation framework in the spirit of TREC (Text REtrieval Conference), CLEF (Cross-Language Evaluation Forum), and NTCIR (NII Test Collection for IR Systems), for Indian language Information Retrieval. The first evaluation exercise conducted by FIRE was completed in 2008. This article describes the test collections used at FIRE 2008, summarizes the approaches adopted by various participants, discusses the limitations of the datasets, and outlines the tasks planned for the next iteration of FIRE.

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