Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Cross-lingual relevance models
SIGIR '02 Proceedings of the 25th 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
Computational Linguistics - Special issue on web as corpus
An IR approach for translating new words from nonparallel, comparable texts
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Base Noun Phrase translation using web data and the EM algorithm
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Mining key phrase translations from web corpora
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Cross-lingual query suggestion using query logs of different languages
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Mining web query hierarchies from clickthrough data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
| Hi-index | 0.00 |
Query translation for Cross-Lingual Information Retrieval (CLIR) has gained increasing attention in the research area. Previous work mainly used machine translation systems, bilingual dictionaries, or web corpora to perform query translation. However, most of these approaches require either expensive language resources or complex language models, and cannot achieve timely translation for new queries. In this paper, we propose a novel solution to automatically acquire query translation pairs from the knowledge hidden in the click-through data, that are represented by the URL a user clicks after submitting a query to a search engine. Our proposed solution consists of two stages: identitying bilingual URL pair patterns in the click-through data and matching query translation pairs based on user click behavior. Experimental results on a real dataset show that our method not only generates existing query translation pairs with high precision, but also generates many timely query translation pairs that could not be obtained by previous methods. A comparative study between our system and two commercial online translation systems shows the advantage of our proposed method.