Conference Paper

ENSM-SE at CLEF 2005: Using a Fuzzy Proximity Matching Function.

DOI: 10.1007/11878773_21 Conference: Accessing Multilingual Information Repositories, 6th Workshop of the Cross-Language Evalution Forum, CLEF 2005, Vienna, Austria, 21-23 September, 2005, Revised Selected Papers
Source: DBLP

ABSTRACT Starting from the idea that the closer the query terms in a document are to each other the more relevant the document, we propose an information retrieval method that uses the degree of fuzzy proximity of key terms in a document to compute the relevance of the document to the query. Our model handles Boolean queries but, contrary to the traditional extensions of the basic Boolean information retrieval model, does not use a proximity operator explicitly. A single parameter makes it possible to control the proximity degree required. We explain how we construct the queries and report the results of our experiments in the ad-hoc monolingual French task of the CLEF 2005 evaluation campaign.

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    ABSTRACT: We investigate the application of a novel relevance ranking technique, cover density ranking, to the requirements of Web-based information retrieval, where a typical query consists of a few search terms and a typical result consists of a page indicating several potentially relevant documents. Traditional ranking methods for information retrieval, based on term and inverse document frequencies, have been found to work poorly in this context. Under the cover density measure, ranking is based on term proximity and cooccurrence. Experimental comparisons show performance that compares favorably with previous work.
    Information Processing & Management 03/2000; · 1.07 Impact Factor
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    ABSTRACT: This paper suggests the use of proximity measurement in combination with the Okapi probabilistic model. First, using the Okapi system, our investigation was carried out in a distributed retrieval framework to calculate the same relevance score as that achieved by a single centralized index. Second, by applying a term-proximity scoring heuristic to the top documents returned by a keyword-based system, our aim is to enhance retrieval performance. Our experiments were conducted using the TREC8, TREC9 and TREC10 test collections, and show that the suggested approach is stable and generally tends to improve retrieval effectiveness especially at the top documents retrieved.
    12/2002: pages 79-79;
  • Proceedings of the 17th Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. Dublin, Ireland, 3-6 July 1994 (Special Issue of the SIGIR Forum); 01/1994

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