Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1, which uses explicitly modeling of the s...
Organizing Web search results into clusters facilitates users' quick browsing through search results. Traditional clustering techniques are inadequate since they don't g...
In this paper we study the problem of finding most topical named entities among all entities in a document, which we refer to as focused named entity recognition. We show that th...
Knowledge Sifter is a scaleable agent-based system that supports access to heterogeneous information sources such as the Web, open-source repositories, XML-databases and the emergi...
Larry Kerschberg, Mizan Chowdhury, Alberto Damiano...
Queries over XML documents challenge search engines to return the most relevant XML components that satisfy the query concepts. In a previous work[6] we described an algorithm to ...