document: Modeling click-through based word-pairs for web search 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 .
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