Clickthrough-based translation models for web search: from word models to phrase models
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Clickthrough-based latent semantic models for web search
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Extracting multilingual topics from unaligned comparable corpora
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Modeling click-through based word-pairs for web search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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Statistical translation models and latent semantic analysis (LSA) are two effective approaches to exploit click-through data for web search ranking. This paper presents two document ranking models that combine both approaches by explicitly modeling word-pairs. The first model, called PairModel, is a monolingual ranking model based on word pairs that are derived from click-through data. It maps queries and documents into a concept space spanned by these word pairs. The second model, called Bilingual Paired Topic Model (BPTM), uses bilingual word pairs and jointly models a bilingual query-document collection. This model maps queries and documents in multiple languages into a lower dimensional semantic subspace. Experimental results on web search task show that they significantly outperform the state-of-the-art baseline models, and the best result is obtained by interpolating PairModel and BPTM.