Proper name translation in cross-language information retrieval

Chen, Hsin-Hsi; Hueng, Sheng-Jie; Ding, Yung-Wei; Tsai, Shih‐Chuan

doi:10.3115/980451.980883

Cited by 8 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because proper names are usually unknown words, it is hard to find in a monolingual dictionary not to mention a bilingual dictionary. MTIR incorporates a machine transliteration algorithm (Chen, Huang, Ding, & Tsai, 1998) to deal with this problem.…”

Section: Proper Name Translationmentioning

confidence: 99%

Cross-language information access to multilingual collections on the internet

Bian

Chen

2000

J. Am. Soc. Info. Sci.

Self Cite

View full text Add to dashboard Cite

Language barrier is the major problem that people face in searching for, retrieving, and understanding multilingual collections on the Internet. This paper deals with query translation and document translation in a Chinese‐English information retrieval system called MTIR. Bilingual dictionary and monolingual corpus‐based approaches are adopted to select suitable translated query terms. A machine transliteration algorithm is introduced to resolve proper name searching. We consider several design issues for document translation, including which material is translated, what roles the HTML tags play in translation, what the tradeoff is between the speed performance and the translation performance, and what form the translated result is presented in. About 100,000 Web pages translated in the last four months of 1997 are used for quantitative study of online and real‐time Web page translation.

show abstract

Section: Proper Name Translationmentioning

confidence: 99%

Cross-language information access to multilingual collections on the internet

Bian

Chen

2000

J. Am. Soc. Info. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The importance of the problem of name translation in cross-language tasks such as Machine Translation (MT) and Cross-Language Information Retrieval (CLIR) is well recognized by the Human Language Technology community (Chen et al 1998), (Virga and Khudanpur 2003). In Machine Translation, many of the out-of-vocabulary words are names and "name dropping" and mis-translation of names degrade the quality of the translated text (Hermjakob, Knight, and Iii 2008).…”

Section: Introductionmentioning

confidence: 99%

PR + RQ ≈ PQ: Transliteration Mining Using Bridge Language

Khapra

Udupa

Kumaran

et al. 2010

AAAI

View full text Add to dashboard Cite

We address the problem of mining name transliterations from comparable corpora in languages P and Q in the following resource-poor scenario:Parallel names in PQ are not available for training. Parallel names in PR and RQ are available for training.We propose a novel solution for the problem by computing a common geometric feature space for P,Q and R where name transliterations are mapped to similar vectors. We employ Canonical Correlation Analysis (CCA) to compute the common geometric feature space using only parallel names in PR and RQ and without requiring parallel names in PQ. We test our algorithm on data sets in several languages and show that it gives results comparable to the state-of-the-art transliteration mining algorithms that use parallel names in PQ for training.

show abstract

“…Recently, much research has been done on machine transliteration for many language pairs, such as English‐Arabic (Al‐Onaizan & Knight, 2002; Stalls & Knight, 1998), English‐Chinese (Chen, Huang, Ding, & Tsai, 1998; Huang, Zhang, & Vogel, 2005; Lee, Chang, & Jang, 2006; Lin & Chen, 2002; Wan & Verspoor, 1998; Sproat, Tao, & Zhai, 2006), English‐Japanese (Knight & Graehl, 1998; Jong & Key, 2006), and English‐Korean (Jeong, Myaeng, Lee, & Choi, 1999; Jong & Key, 2006; Kang & Choi, 2001; Oh & Choi, 2006). Most of the above approaches require a pronunciation dictionary for converting a source word into a sequence of pronunciations.…”

Section: Introductionmentioning

confidence: 99%

English‐Arabic proper‐noun transliteration‐pairs creation

Fattah

Ren

2008

J. Am. Soc. Inf. Sci.

View full text Add to dashboard Cite

Proper nouns may be considered the most important query words in information retrieval. If the two languages use the same alphabet, the same proper nouns can be found in either language. However, if the two languages use different alphabets, the names must be transliterated. Short vowels are not usually marked on Arabic words in almost all Arabic documents (except very important documents like the Muslim and Christian holy books).Moreover, most Arabic words have a syllable consisting of a consonant-vowel combination (CV), which means that most Arabic words contain a short or long vowel between two successive consonant letters. That makes it difficult to create English-Arabic transliteration pairs, since some English letters may not be matched with any romanized Arabic letter. In the present study, we present different approaches for extraction of transliteration proper-noun pairs from parallel corpora based on different similarity measures between the English and romanized Arabic proper nouns under consideration.The strength of our new system is that it works well for low-frequency proper noun pairs. We evaluate the new approaches presented using two different English-Arabic parallel corpora. Most of our results outperform previously published results in terms of precision, recall, and F -Measure.

show abstract

Proper name translation in cross-language information retrieval

Cited by 8 publications

References 0 publications

Cross-language information access to multilingual collections on the internet

Cross-language information access to multilingual collections on the internet

PR + RQ ≈ PQ: Transliteration Mining Using Bridge Language

English‐Arabic proper‐noun transliteration‐pairs creation

Contact Info

Product

Resources

About

Proper name translation in cross-language information retrieval

Cited by 8 publications

References 0 publications

Cross-language information access to multilingual collections on the internet

Cross-language information access to multilingual collections on the internet

PR + RQ &asymp; PQ: Transliteration Mining Using Bridge Language

English‐Arabic proper‐noun transliteration‐pairs creation

Contact Info

Product

Resources

About

PR + RQ ≈ PQ: Transliteration Mining Using Bridge Language