Given a drug under development, what are other drugs or biochemical compounds that it might interact with? Early answers to this question, by mining the literature, are valuable for pharmaceutical companies, both monetarily and in avoiding public relations nightmares. Inferring drug-drug interactions is also important in designing combination therapies for complex diseases including cancers. We study this problem as one of mining linguistic cues for query expansion. By using (only) positive instances of drug interactions, we show how we can extract linguistic cues which can then be used to expand and reformulate queries to improve the effectiveness of drug interaction search. Our approach integrates many learning paradigms: partially supervised classification, association measures for collocation mining, and feature selection in supervised learning. We demonstrate compelling results on using positive examples from the DrugBank database to seed MEDLINE searches for drug interactions. In particular, we show that purely data-driven linguistic cues can be effectively mined and applied to realize a successful domain-specific query expansion framework.

Query expansion is a well-known method for improving average effectiveness in information retrieval. The most effective query expansion methods rely on retrieving documents which are used as a source of expansion terms. Retrieving those documents is costly. We examine the bottlenecks of a conventional approach and investigate alternative methods aimed at reducing query evaluation time. We propose a new method that draws candidate terms from brief document summaries that are held in memory for each document. While approximately maintaining the effectiveness of the conventional approach, this method significantly reduces the time required for query expansion by a factor of five to ten.

This paper proposes the use of multiple thesaurus types for query expansion in information retrieval. Hand-crafted thesaurus, corpus-based co-occurrence-based thesaurus and syntactic-relation-based thesaurus are combined and used as a tool for query expansion. A simple word sense disambiguation is performed to avoid misleading expansion terms. Experiments using TREC-7 collection proved that this method could improve the information retrieval performance significantly. Failure analysis was done on the cases in which the proposed method fail to improve the retrieval effectiveness. We found that queries containing negative statements and multiple aspects might cause problems in the proposed method.

This paper describes the experimentation conducted to test the effectiveness of query expansion within the logical model PLBR. We ran different experiments generating queries as logical formulas with different connectives, and using different types of linguistic information extracted from WordNet. Results show that lexical expansion is not able to improve retrieval performance. Nevertheless, the experiments allow us to conclude that query expansion can benefit from a logical model which allows structured queries.

Medical free-text queries often share the same scenario. A scenario represents a repeating task in healthcare. For example, a specific scenario is searching for treatment methods for a specific dis-ease, where “treatment ” is a term indicating the scenario. To support scenario-specific retrieval, in this paper we present a new knowledge-based approach to address these problems. In addition, we describe a testbed system developed using the approach. Our specific implementation uses the UMLS Metathesaurus and semantic structure to extract key concepts from a free-text. The ap-proach uses phrase-based indexing to represent similar concepts, and query expansion to improve matching query terms with the terms in the document. The system formulates the query based on the user’s input and the selected scenario template such as “disease, treatment ” or “disease, diagno-sis. ” Thus, it is able to retrieve documents relevant to the specific scenario. Evaluating the system using the standard OSHMED corpus, our empirical results validate the effectiveness of this new approach over the traditional text retrieval techniques. A.

As information volume in enterprise systems and in the Web grows rapidly, how to accurately retrieve information is an important research area. Several corpus based smoothing techniques have been proposed to address the data sparsity and synonym problems faced by information retrieval systems. Such smoothing techniques are often unable to discover and utilize the correlations among terms.

This paper proposes the use of multiple thesaurus types for query expansion in information retrieval. Hand-crafted thesaurus, corpus-based co-occurrence-based thesaurus and syntactic-relation-based thesaurus are combined and used as a tool for query expansion. A simple word sense disambiguation is performed to avoid misleading expansion terms. Experiments using TREC-7 collection proved that this method could improve the information retrieval performance significantly. Failure analysis was done on the cases in which the proposed method fail to improve the retrieval effectiveness. We found that queries containing negative statements and multiple aspects might cause problems in the proposed method.

This thesis investigates the appropriateness of using lexical cohesion analysis to improve the performance of Information Retrieval (IR) and Natural Language Processing (NLP) applications that deal with documents in the news domain. This thesis reports on the performance of some challenging, real-world applications of lexical cohesion analysis with respect to the performance of ‘bag-of-words’ approaches to these problems. In particular, we attempt to enhance New Event Detection and News Story Segmentation performance: two tasks currently being investigated by the Topic Detection and Tracking (TDT) initiative, a research programme dedicated to the intelligent organisation of broadcast news and newswire data streams.

Abstract: Based on the query expansion techniques in information retrieval systems, structure-based query expansion for XML search engines, which is designed to ease the query for XML data while keeping the power and flexibility of XML query, is introduced in this paper. To enable the structure expansion, a structure thesaurus should be built first, which involves the construction of a weighted graph from XML documents and the linkage-based clustering method to cluster the nodes into several groups. After a query comes, the structure thesaurus is examined, so that for each tag in the original query, the tags in the same group are retrieved. Unrelated tags are filtered and some heuristic rules are applied to replacing the tags in the original query with the related tags and to expanding the structure. It is shown that using structure-based query expansion, the system can return result with high precision and recall. 1.

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