The recognition of names and their associated categories within unstructured text
traditionally relies on semantic lexicons and gazetteers. The amount of effort required to …

Document clustering and classification is usually done by representing the documents using
a bag of words scheme. This scheme ignores many of the linguistic and semantic features …

This paper presents an approach to extending existing lexical resources with instance
names and alternative definitions acquired from textual documents. The experiments involve …

We develop an algorithmic framework to decompose a collection of time-stamped text
documents into semantically coherent threads. Our formulation leads to a graph …

The output of biomedical research is largely documented as findings in the form of literature
written in free-form text format (Nenadic, Mima, Spasic, Ananiadou, & Tsujii, 2002). The …

We propose a novel approach to classify documents into different categories using lexical
chaining. In this paper we present a text categorization technique that extracts lexical …

This paper presents an approach to extending existing lexical resources with instance
names and alternative definitions acquired from textual documents. The experiments involve …

How can search engines use the hyperlinks between documents to determine which
documents are the most relevant for a search query? Some search engines use links to …

Automatic classification and clustering are two of the most common operations performed on
text documents. Numerous algorithms have been proposed for this and invariably, all of …