We introduce two heterogeneous query expansion techniques, and a combined system to the TREC 2012 Medical Track. Our methods are based on external resources that provide expansion concepts related to the query terms, by means of the PageRank algorithm, and simple rules based on UMLS Semantic Types. In this paper we show that our systems are able to reach competitive performances at both the TRE...

This paper explores log-based query expansion (QE) models for Web search. Three lexicon models are proposed to bridge the lexical gap between Web documents and user queries. These models are trained on pairs of user queries and titles of clicked documents. Evaluations on a real world data set show that the lexicon models, integrated into a ranker-based QE system, not only significantly improve ...

Automatic query expansion is well known as a technique that improves query effectiveness on average. Unfortunately, it is usually very slow, increasing the time to process a query by 20 times or more. In this study, we use relevance models to show how the process can be made almost as fast as running a non-expanded query.

This paper concerns the motivation for and subsequently the analysis of proposed additions, in the form of new operators, to a concept language Ontolog for use in querying a content-based text retrieval system. The expressiveness of the proposed query language introduces the possibility for querying both objects in the base but also mechanisms for direct querying of the ontology, and it further...

Retrieving answer containing passages is a challenging task in Question Answering. In this paper we describe a novel query expansion method which aims to rank the answer containing passages better. It uses content and structured information (link structure and category information) of Wikipedia to generate a set of terms semantically related to the question. As Boolean model allows a fine-grain...

Term cooccurrence data has been extensively used in document retrieval systems for the identification of indexing terms that are similar to those that have been specified in a user query: these similar terms can then be used to augment the original query statement. Despite the plausibility of this approach to query expansion, the retrieval effectiveness of the expanded queries is often no great...

Our research aim is to ameliorate the recall of the Fuzzy Proximity Information Retrieval Model (FPIRM) of Beigbeder & Mercier (2005), their approach is very “precise” when evaluating internationally agreed upon collections of documents used for benchmarking. The precision in this case for each query is the ratio of relevant documents retrieved over documents returned. However, the recall is we...

This poster presents an ongoing work on using biomedical ontologies to improve efficiency on information retrieval.

A number of studies have examined the problems of query expansion in monolingual IR, and query translation for cross-language IR. However, no link has been made between them. This paper first shows that query translation is a special case of query expansion. There is also another set of studies on inferential IR. Again, there is no relationship established with query translation or query expans...

Query expansion (QE) improves the retrieval quality in average, even though it can dramatically decrease performance for certain queries. This observation drives the trend to suggest selective approaches that aim at choosing the best function to apply for each query. Most of selective approaches use a learning process on past query features and results. This paper presents a new selective QE me...