OcrV1, Main, Exploration, bibRecord, 000466

LIA at INEX 2010 Book Track

Identifieur interne : 000466 ( Main/Exploration ); précédent : 000465; suivant : 000467

LIA at INEX 2010 Book Track

Auteurs : Romain Deveaud [France] ; Florian Boudin [France] ; Patrice Bellot [France]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.

RBID : ISTEX:32C64BBE549F217A397CA75E9277439F6E1489AB

Abstract

Abstract: In this paper we describe our participation and present our contributions in the INEX 2010 Book Track. Digitized books are now a common source of information on the Web, however OCR sometimes introduces errors that can penalize Information Retrieval. We propose a method for correcting hyphenations in the books and we analyse its impact on the Best Books for Reference task. The observed improvement is around 1%. This year we also experimented different query expansion techniques. The first one consists of selecting informative words from a Wikipedia page related to the topic. The second one uses a dependency parser to enrich the query with the detected phrases using a Markov Random Field model. We show that there is a significant improvement over the state-of-the-art when using a large weighted list of Wikipedia words, meanwhile hyphenation correction has an impact on their distribution over the book corpus.

Url:

DOI: 10.1007/978-3-642-23577-1_10

Affiliations:

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<front><div type="abstract" xml:lang="en">Abstract: In this paper we describe our participation and present our contributions in the INEX 2010 Book Track. Digitized books are now a common source of information on the Web, however OCR sometimes introduces errors that can penalize Information Retrieval. We propose a method for correcting hyphenations in the books and we analyse its impact on the Best Books for Reference task. The observed improvement is around 1%. This year we also experimented different query expansion techniques. The first one consists of selecting informative words from a Wikipedia page related to the topic. The second one uses a dependency parser to enrich the query with the detected phrases using a Markov Random Field model. We show that there is a significant improvement over the state-of-the-art when using a large weighted list of Wikipedia words, meanwhile hyphenation correction has an impact on their distribution over the book corpus.</div>
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LIA at INEX 2010 Book Track

LIA at INEX 2010 Book Track

Source :

Abstract

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