Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora

Georgios Kontonatsios; Yannis Korkontzelos; Jun'ichi Tsujii; Sophia Ananiadou

Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora

Georgios Kontonatsios, Yannis Korkontzelos, Jun'ichi Tsujii, Sophia Ananiadou

Computer Science

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

15 Citations (Scopus)

98 Downloads (Pure)

Abstract

We describe a machine learning approach, a Random Forest (RF) classifier, that is used to automatically compile bilingual dictionaries of technical terms from comparable corpora. We evaluate the RF classifier against a popular term alignment method, namely context vectors, and we report an improvement of the translation accuracy. As an application, we use the automatically extracted dictionary in combination with a trained Statistical Machine Translation (SMT) system to more accurately translate unknown terms. The dictionary extraction method described in this paper is freely available.

Original language	English
Pages	111-116
Publication status	Published - Apr 2014
Event	14th Conference of the European Chapter of the Association for Computational Linguistics - Gothenburg, Sweden Duration: 26 Apr 2014 → 30 Apr 2014

Conference

Conference	14th Conference of the European Chapter of the Association for Computational Linguistics
Country/Territory	Sweden
City	Gothenburg
Period	26/04/14 → 30/04/14

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E14-4022.pdfOther version, 155 KBLicence: CC BY-NC-SA

http://www.aclweb.org/anthology/E14-4022

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title = "Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora",

abstract = "We describe a machine learning approach, a Random Forest (RF) classifier, that is used to automatically compile bilingual dictionaries of technical terms from comparable corpora. We evaluate the RF classifier against a popular term alignment method, namely context vectors, and we report an improvement of the translation accuracy. As an application, we use the automatically extracted dictionary in combination with a trained Statistical Machine Translation (SMT) system to more accurately translate unknown terms. The dictionary extraction method described in this paper is freely available.",

author = "Georgios Kontonatsios and Yannis Korkontzelos and Jun'ichi Tsujii and Sophia Ananiadou",

year = "2014",

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note = "14th Conference of the European Chapter of the Association for Computational Linguistics ; Conference date: 26-04-2014 Through 30-04-2014",

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Kontonatsios, G, Korkontzelos, Y, Tsujii, J & Ananiadou, S 2014, 'Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora', Paper presented at 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden, 26/04/14 - 30/04/14 pp. 111-116. <http://www.aclweb.org/anthology/E14-4022>

Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora. / Kontonatsios, Georgios; Korkontzelos, Yannis; Tsujii, Jun'ichi et al.
2014. 111-116 Paper presented at 14th Conference of the European Chapter of the Association for Computational Linguistics, Gothenburg, Sweden.

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

TY - CONF

T1 - Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora

AU - Kontonatsios, Georgios

AU - Korkontzelos, Yannis

AU - Tsujii, Jun'ichi

AU - Ananiadou, Sophia

PY - 2014/4

Y1 - 2014/4

N2 - We describe a machine learning approach, a Random Forest (RF) classifier, that is used to automatically compile bilingual dictionaries of technical terms from comparable corpora. We evaluate the RF classifier against a popular term alignment method, namely context vectors, and we report an improvement of the translation accuracy. As an application, we use the automatically extracted dictionary in combination with a trained Statistical Machine Translation (SMT) system to more accurately translate unknown terms. The dictionary extraction method described in this paper is freely available.

AB - We describe a machine learning approach, a Random Forest (RF) classifier, that is used to automatically compile bilingual dictionaries of technical terms from comparable corpora. We evaluate the RF classifier against a popular term alignment method, namely context vectors, and we report an improvement of the translation accuracy. As an application, we use the automatically extracted dictionary in combination with a trained Statistical Machine Translation (SMT) system to more accurately translate unknown terms. The dictionary extraction method described in this paper is freely available.

M3 - Paper

SP - 111

EP - 116

T2 - 14th Conference of the European Chapter of the Association for Computational Linguistics

Y2 - 26 April 2014 through 30 April 2014

ER -

Using a Random Forest Classifier to Compile Bilingual Dictionaries of Technical Terms from Comparable Corpora

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Conference

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