Context influences the search process, but to date research has not definitively identified which aspects of context are the most influential for information retrieval, and thus a...
Luanne Freund, Elaine G. Toms, Charles L. A. Clark...
Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) have been successfully applied to a number of text analysis tasks such as docum...
This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate a...
In this paper, we propose a novel dependency language modeling approach for information retrieval. The approach extends the existing language modeling approach by relaxing the ind...
In this poster we describe alternative inverted index structures that reduce the time required to process queries, produce a higher query throughput and still return high quality ...
Paul Ferguson, Alan F. Smeaton, Cathal Gurrin, Pet...
Existing retrieval models generally do not offer any guarantee for optimal retrieval performance. Indeed, it is even difficult, if not impossible, to predict a model’s empirica...
Summative evaluation methods for supervised adaptive topic tracking systems convolve the effect of system decisions on present utility with the effect on future utility. This pa...
To investigate the nature of people’s understandings for how search engines work, we collected data from 232 undergraduate and graduate students. Students were asked to “draw ...
State-of-the-art question answering (QA) systems employ termdensity ranking to retrieve answer passages. Such methods often retrieve incorrect passages as relationships among ques...
Hang Cui, Renxu Sun, Keya Li, Min-Yen Kan, Tat-Sen...