In response to a query a search engine returns a ranked list of documents. If the query is on a popular topic (i.e., it matches many documents) then the returned list is usually t...
In this paper, we propose a new strategy with time granularity reasoning for utilizing temporal information in topic tracking. Compared with previous ones, our work has four disti...
We consider the use of Bayesian topic models in the analysis of computer network traffic. Our approach utilizes latent Dirichlet allocation and time-varying dynamic latent Dirich...
This paper evaluates to which extent Google retrieved correct answers as responses to queries inferred from factual and topical requests in a digital Ask-a-Librarian service. 100 f...
We investigate the topical structure of the set of documents used to expand a query in pseudorelevance feedback (PRF). We propose a coherence score to measure the relative topical...