We describe our participation in the CLEF 2008 Domain Specific track. The research questions we address are threefold: (i) what are the effects of estimating and applying relevance models to the domain specific collection used at CLEF 2008, (ii) what are the results of parsimonizing these relevance models, and (iii) what are the results of applying concept models for blind relevance feedback? P...

Two probabilistic approaches to cross-lingual retrieval are in wide use today, those based on probabilistic models of relevance, as exemplified by INQUERY, and those based on language modeling. INQUERY, as a query net model, allows the easy incorporation of query operators, including a synonym operator, which has proven to be extremely useful in cross-language information retrieval (CLIR), in a...

We describe our participation in the CLEF 2008 Domain Specific track. The research questions we address are threefold: (i) what are the effects of estimating and applying relevance models to the domain specific collection used at CLEF 2008, (ii) what are the results of parsimonizing these relevance models, and (iii) what are the results of applying concept models for blind relevance feedback? P...

Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user’s judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the firs...

Feedback is an important technique in Information Retrieval to have users provide contextual information about their search needs, with the goal of improving retrieval accuracy and achieving personalization. Relevance feedback has been studied extensively, and in recent years new types of feedback such as implicit feedback and collective feedback have attracted much research interest. However, ...

In this paper, we investigate the relationship between smoothing in language models and idf weights. Language models regard the relative within-document-frequency and the relative collection frequency; idf weights are very similar to the latter, but yield higher weights for rare terms. Regarding the correlation between the language model parameters and relevance for two test collections, we fin...

Over the years, various meta-languages have been used to manually enrich documents with conceptual knowledge of some kind. Examples include keyword assignment to citations or, more recently, tags to websites. In this paper we propose generative concept models as an extension to query modeling within the language modeling framework, which leverages these conceptual annotations to improve retriev...

We show that several previously proposed passage-based document ranking principles, along with some new ones, can be derived from the same probabilistic model. We use language models to instantiate specific algorithms, and propose a passage language model that integrates information from the ambient document to an extent controlled by the estimated document homogeneity. Several document-homogen...

In this paper, the relevance factor in maximum a posteriori (MAP) adaptation of Gaussian mixture model (GMM) from universal background model (UBM) is studied for language recognition. In conventional MAP, relevance factor is typically set as a constant empirically. Knowing that relevance factor determines how much the observed training data influence the model adaptation, thus the resulting GMM...