Information Retrieval (IR) refers to retrieving relevant documents from a large document database according to a user-submitted query, and is among the most useful technologies to overcome information overload. For example, Web search engines are now essential tools for everyone to find information on the Web. Indeed, search capabilities are becoming more and more popular in virtually all kinds...

Language models are usually evaluated on test texts using the perplexity derived directly from the model likelihood function. In order to use this measure in the framework of a comparative evaluation campaign, we have developped an alternative scheme for perplexity estimation. The method is derived from the Shannon game and based on a gambling approach on the next word to come in a truncated se...

Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the Pseudo Relevance Feedback task. It is known that one of the factors that more affect to the Pseudo Relevance Fee...

language is primarily constituted by action and interaction based on sensorimotor information. this paper demonstrates the nature of subjectivity and intersubjectivity through the neural mechanism and typological evidence of sentence-final particles from east asian languages and extends to the discussion of the relationship between them. i propose that intersubjecivity is a kind of embedded or ...

We describe a method for applying parsimonious language models to re-estimate the term probabilities assigned by relevance models. We apply our method to six topic sets from test collections in five different genres. Our parsimonious relevance models (i) improve retrieval effectiveness in terms of MAP on all collections, (ii) significantly outperform their non-parsimonious counterparts on most ...

This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed...