A transitive model for extracting translation equivalents of web queries through anchor text mining

Authors:
Wen-Hsiang Lu;Lee-Feng Chien;Hsi-Jian Lee
Affiliations:
National Chiao Tung University, Taiwan, ROC;Institute of Information Science, Taiwan, ROC;National Chiao Tung University, Hsinchu, Taiwan, ROC
Venue:
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Year:
2002

Citing 14
Cited 1

On modeling information retrieval with probabilistic inference

ACM Transactions on Information Systems (TOIS)
Translating collocations for bilingual lexicons: a statistical approach

Computational Linguistics
PAT-tree-based keyword extraction for Chinese information retrieval

Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Phrasal translation and query expansion techniques for cross-language information retrieval

Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
QUILT: implementing a large-scale cross-language text retrieval system

Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic resource compilation by analyzing hyperlink structure and associated text

WWW7 Proceedings of the seventh international conference on World Wide Web 7
Cross-language information retrieval based on parallel texts and automatic mining of parallel texts from the Web

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment

Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Improving cross language retrieval with triangulated translation

Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Anchor Text Mining for Translation of Web Queries

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The mathematics of statistical machine translation: parameter estimation

Computational Linguistics - Special issue on using large corpora: II
An IR approach for translating new words from nonparallel, comparable texts

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
You'll take the high road and I'll take the low road: using a third language to improve bilingual word alignment

COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic identification of word translations from unrelated English and German corpora

ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics

Anchor text mining for translation of Web queries: A transitive translation approach

ACM Transactions on Information Systems (TOIS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

One of the existing difficulties of cross-language information retrieval (CLIR) and Web search is the lack of appropriate translations of new terminology and proper names. Different from conventional approaches, in our previous research we developed an approach for exploiting Web anchor texts as live bilingual corpora and reducing the existing difficulties of query term translation. Although Web anchor texts, undoubtedly, are very valuable multilingual and wide-scoped hypertext resources, not every particular pair of languages contains sufficient anchor texts in the Web to extract corresponding translations in the language pair. For more generalized applications, in this paper we extend our previous approach by adding a phase of transitive (indirect) translation via an intermediate (third) language, and propose a transitive model to further exploit anchor-text mining in term translation extraction applications. Preliminary experimental results show that many query translations which cannot be obtained using the previous approach can be extracted with the improved approach.