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


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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Available from: Annabelle Mercier, Sep 16, 2014
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    ABSTRACT: Cross-lingual spoken sentence retrieval (CLSSR) remains a challenge, especially for queries including OOV words such as person names. This paper proposes a simple method of fuzzy matching between query names and phones of candidate audio segments. This approach has the advantage of avoiding some word decoding errors in automatic speech recognition (ASR). Experiments on Mandarin-English CLSSR show that phone-based searching and conventional translation-based searching are complementary. Adding phone matching achieved 26.29% improvement on F-measure over searching on state-of-the-art machine translation (MT) output and 8.83% over entity translation (ET) output.
    Spoken Language Technology Workshop, 2008. SLT 2008. IEEE; 01/2009