Data driven methods for improving mono- and cross-lingual IR performance in noisy environments

Järvelin, Antti; Talvensaari, Tuomas; Järvelin, Anni

doi:10.1145/1390749.1390762

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…The notion of skipgram (McNamee, 2008), also referred to as gap-n-gram (Mustafa, 2005) or s-gram (Järvelin et al, 2008) by other authors, is a generalization of the concept of n-gram by allowing skips during the matching process. However, McNamee (2008) showed that skipgrams are dramatically more costly than traditional n-grams and, while performing reasonably well, they are not demonstrably more effective.…”

Section: The N-gram Based Approachmentioning

confidence: 99%

Managing misspelled queries in IR applications

Vilares

Otero

2011

Information Processing & Management

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Section: The N-gram Based Approachmentioning

confidence: 99%

Managing misspelled queries in IR applications

Vilares

Otero

2011

Information Processing & Management

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“…Classic Vector Space and Probabilistic models (Manning et al, 2008) are the first options. However, the very special and noisy nature of Egyptian writing system and the application context may suggest the use of other approaches: the use of standard character n-grams as a working unit, a solution successfully applied in both noisy contexts (Vilares et al, 2011) and languages whose writing systems share characteristics with Egyptian, such as Japanese (Ogawa and Matsuda, 1999), Chinese (Foo and Li, 2004), Korean (Lee and Ahn, 1996) or Arabic (Mustafa and Al-Radaideh, 2004); the use of so-called character s-grams (Järvelin et al, 2008), a generalization of the concept of n-gram by allowing skips during the matching process; the application of localitybased models (de Kretser and Moffat, 1999); or phonetic matching (Yasukawa et al, 2012). Closer to the NLP field, the development of conflation mechanisms based on lemmatization or morphological analysis (Piotrowski, 2012, Ch.…”

Section: Discussionmentioning

confidence: 99%

Searching Four-Millenia-Old Digitized Documents: A Text Retrieval System for Egyptologists

Iglesias-Franjo¹,

Vilares

2016

Proceedings of the 10th SIGHUM Workshop on Language Technology For Cultural Heritage, Social Sciences, and Humanities

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Progress made in recent years has led to a growing interest in Digital Heritage. This article focuses on Egyptology and, more specifically, the study and preservation of ancient Egyptian scripts. We present a Text Retrieval system developed specifically to work with hieroglyphic texts. We intend to make it freely available to the research community. To the best of our knowledge this is the first tool of its kind.

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“…Other IR-related, but more complex, application of n-grams are the use of skipgrams (McNamee, 2008), also referred to as gap-n-grams (Mustafa, 2005) or s-grams (Järvelin et al, 2008) by other authors. This is a generalization of the concept of n-gram by allowing skips during the matching process.…”

Section: Background and Related Workmentioning

confidence: 99%

On the feasibility of character n-grams pseudo-translation for Cross-Language Information Retrieval tasks

Vilares

Alonso

et al. 2016

Computer Speech & Language

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The field of Cross-Language Information Retrieval relates techniques close to both the Machine Translation and Information Retrieval fields, although in a context involving characteristics of its own. The present study looks to widen our knowledge about the effectiveness and applicability to that field of nonclassical translation mechanisms that work at character n-gram level. For the purpose of this study, an n-gram based system of this type has been developed. This system requires only a bilingual machine-readable dictionary of n-grams, automatically generated from parallel corpora, which serves to translate queries previously n-grammed in the source language. n-Gramming is then used as an approximate string matching technique to perform monolingual text retrieval on the set of n-grammed documents in the target language.The tests for this work have been performed on CLEF collections for seven European languages, taking English as the target language. The performance attained, close to the upper baseline, confirms the validity of character n-gram based approaches for Cross Language Information Retrieval tasks, both for indexing-retrieval and translation purposes, these not being tied to a given implementation.

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Data driven methods for improving mono- and cross-lingual IR performance in noisy environments

Cited by 4 publications

References 18 publications

Managing misspelled queries in IR applications

Managing misspelled queries in IR applications

Searching Four-Millenia-Old Digitized Documents: A Text Retrieval System for Egyptologists

On the feasibility of character n-grams pseudo-translation for Cross-Language Information Retrieval tasks

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