An empirical study on web mining of parallel data

Gumwon Hong; Chi Ho Li; Ming Zhou; Hae Chang Rim

An empirical study on web mining of parallel data

Gumwon Hong, Chi Ho Li, Ming Zhou, Hae Chang Rim

Research output: Contribution to conference › Paper › peer-review

Abstract

This paper¹ presents an empirical approach to mining parallel corpora. Conventional approaches use a readily available collection of comparable, nonparallel corpora to extract parallel sentences. This paper attempts the much more challenging task of directly searching for high-quality sentence pairs from the Web. We tackle the problem by formulating good search query using Learning to Rank? and by filtering noisy document pairs using IBM Model 1 alignment. End-to-end evaluation shows that the proposed approach significantly improves the performance of statistical machine translation.

Original language	English
Pages	474-482
Number of pages	9
Publication status	Published - 2010
Event	23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China Duration: 2010 Aug 23 → 2010 Aug 27

Other

Other	23rd International Conference on Computational Linguistics, Coling 2010
Country/Territory	China
City	Beijing
Period	10/8/23 → 10/8/27

ASJC Scopus subject areas

Language and Linguistics
Computational Theory and Mathematics
Linguistics and Language

Cite this

@conference{9b7dcaba372746cc8bd148bcf063122a,

title = "An empirical study on web mining of parallel data",

abstract = "This paper1 presents an empirical approach to mining parallel corpora. Conventional approaches use a readily available collection of comparable, nonparallel corpora to extract parallel sentences. This paper attempts the much more challenging task of directly searching for high-quality sentence pairs from the Web. We tackle the problem by formulating good search query using Learning to Rank? and by filtering noisy document pairs using IBM Model 1 alignment. End-to-end evaluation shows that the proposed approach significantly improves the performance of statistical machine translation.",

author = "Gumwon Hong and Li, {Chi Ho} and Ming Zhou and Rim, {Hae Chang}",

year = "2010",

language = "English",

pages = "474--482",

note = "23rd International Conference on Computational Linguistics, Coling 2010 ; Conference date: 23-08-2010 Through 27-08-2010",

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TY - CONF

T1 - An empirical study on web mining of parallel data

AU - Hong, Gumwon

AU - Li, Chi Ho

AU - Zhou, Ming

AU - Rim, Hae Chang

PY - 2010

Y1 - 2010

N2 - This paper1 presents an empirical approach to mining parallel corpora. Conventional approaches use a readily available collection of comparable, nonparallel corpora to extract parallel sentences. This paper attempts the much more challenging task of directly searching for high-quality sentence pairs from the Web. We tackle the problem by formulating good search query using Learning to Rank? and by filtering noisy document pairs using IBM Model 1 alignment. End-to-end evaluation shows that the proposed approach significantly improves the performance of statistical machine translation.

AB - This paper1 presents an empirical approach to mining parallel corpora. Conventional approaches use a readily available collection of comparable, nonparallel corpora to extract parallel sentences. This paper attempts the much more challenging task of directly searching for high-quality sentence pairs from the Web. We tackle the problem by formulating good search query using Learning to Rank? and by filtering noisy document pairs using IBM Model 1 alignment. End-to-end evaluation shows that the proposed approach significantly improves the performance of statistical machine translation.

UR - http://www.scopus.com/inward/record.url?scp=80053393112&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:80053393112

SP - 474

EP - 482

T2 - 23rd International Conference on Computational Linguistics, Coling 2010

Y2 - 23 August 2010 through 27 August 2010

ER -

An empirical study on web mining of parallel data

Abstract

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ASJC Scopus subject areas

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