Extraction of lexical translations from non-aligned corpora

Tanaka, Kumiko; Iwasaki, Hideya

doi:10.3115/993268.993270

Cited by 57 publications

(38 citation statements)

References 5 publications

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“…The general idea of exploiting time correlations to acquire word translations from comparable corpora has been explored in several previous studies -e.g., (Fung, 1995;Rapp, 1995;Tanaka and Iwasaki, 1996). Recently, a Pearson correlation method was proposed to mine word pairs from comparable corpora (Tao and Zhai, 2005); this idea is similar to the method used in (Kay and Roscheisen, 1993) for sentence alignment.…”

Section: Previous Workmentioning

confidence: 96%

“…Comparable corpora have been studied extensively in the literature -e.g., (Fung, 1995;Rapp, 1995;Tanaka and Iwasaki, 1996;Franz et al, 1998;Ballesteros and Croft, 1998;Masuichi et al, 2000;Sadat et al, 2004), but transliteration in the context of comparable corpora has not been well addressed. The general idea of exploiting time correlations to acquire word translations from comparable corpora has been explored in several previous studies -e.g., (Fung, 1995;Rapp, 1995;Tanaka and Iwasaki, 1996).…”

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised named entity transliteration using temporal and phonetic correlation

Tao

Yoon

Fister

et al. 2006

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06

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In this paper we investigate unsupervised name transliteration using comparable corpora, corpora where texts in the two languages deal in some of the same topics -and therefore share references to named entities -but are not translations of each other. We present two distinct methods for transliteration, one approach using an unsupervised phonetic transliteration method, and the other using the temporal distribution of candidate pairs. Each of these approaches works quite well, but by combining the approaches one can achieve even better results. We believe that the novelty of our approach lies in the phonetic-based scoring method, which is based on a combination of carefully crafted phonetic features, and empirical results from the pronunciation errors of second-language learners of English. Unlike previous approaches to transliteration, this method can in principle work with any pair of languages in the absence of a training dictionary, provided one has an estimate of the pronunciation of words in text.

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Section: Previous Workmentioning

confidence: 96%

Section: Previous Workmentioning

confidence: 99%

Unsupervised named entity transliteration using temporal and phonetic correlation

Tao

Yoon

Fister

et al. 2006

Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06

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“…However, computational limitation hampered further extension of this method. In 1996, Tannaka and Iwasaki [9] demonstrated how to extract lexical translation candidates from non-aligned corpora using the similar idea. In 1999, this method was developed and improved by Rapp [10].…”

Section: ) Acquiring Translation From Non-parallel Corporamentioning

confidence: 99%

Web-Based Terminology Translation Mining

Fang

Nishino

2005

Lecture Notes in Computer Science

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Abstract. Mining terminology translation from a large amount of Web data can be applied in many fields such as reading/writing assistant, machine translation and cross-language information retrieval. How to find more comprehensive results from the Web and obtain the boundary of candidate translations, and how to remove irrelevant noises and rank the remained candidates are the challenging issues. In this paper, after reviewing and analyzing all possible methods of acquiring translations, a feasible statistics-based method is proposed to mine terminology translation from the Web. In the proposed method, on the basis of an analysis of different forms of term translation distributions, character-based string frequency estimation is presented to construct term translation candidates for exploring more translations and their boundaries, and then sort-based subset deletion and mutual information methods are respectively proposed to deal with subset redundancy information and prefix/suffix redundancy information formed in the process of estimation. Extensive experiments on two test sets of 401 and 3511 English terms validate that our system has better performance.

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“…The general idea of exploiting frequency correlations to acquire word translations from comparable corpora has already been explored in several previous studies (e.g., [6,10,15]). However, none of them has adopted a direct comparison of frequency distributions of candidate words as we do; rather they tend to compute the associations between the words in the same language and then compare association patterns in two different languages.…”

Section: Introductionmentioning

confidence: 99%

“…While comparable corpora have been studied extensively in the existing literature (e.g., [6,10,15,5,2,8,13]), almost all existing work assumes some kind of bilingual dictionary or translation examples to start with. We study how to map words and documents from comparable bilingual corpora without requiring any additional linguistic resources such as a bilingual dictionary.…”

Section: Introductionmentioning

confidence: 99%

Mining comparable bilingual text corpora for cross-language information integration

Tao

Zhai

2005

Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining

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Integrating information in multiple natural languages is a challenging task that often requires manually created linguistic resources such as a bilingual dictionary or examples of direct translations of text. In this paper, we propose a general cross-lingual text mining method that does not rely on any of these resources, but can exploit comparable bilingual text corpora to discover mappings between words and documents in different languages. Comparable text corpora are collections of text documents in different languages that are about similar topics; such text corpora are often naturally available (e.g., news articles in different languages published in the same time period). The main idea of our method is to exploit frequency correlations of words in different languages in the comparable corpora and discover mappings between words in different languages. Such mappings can then be used to further discover mappings between documents in different languages, achieving cross-lingual information integration. Evaluation of the proposed method on a 120MB Chinese-English comparable news collection shows that the proposed method is effective for mapping words and documents in English and Chinese. Since our method only relies on naturally available comparable corpora, it is generally applicable to any language pairs as long as we have comparable corpora.

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Extraction of lexical translations from non-aligned corpora

Cited by 57 publications

References 5 publications

Unsupervised named entity transliteration using temporal and phonetic correlation

Unsupervised named entity transliteration using temporal and phonetic correlation

Web-Based Terminology Translation Mining

Mining comparable bilingual text corpora for cross-language information integration

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