This paper addresses the problem of extending an adaptive information filtering system to
make decisions about the novelty and redundancy of relevant documents. It argues that …

Text classification is a major data mining task. An advanced text classification technique is
known as partially supervised text classification, which can build a text classifier using a …

Previous research on cluster-based retrieval has been inconclusive as to whether it does
bring improved retrieval effectiveness over document-based retrieval. Recent developments …

Previous research in novelty detection has focused on the task of finding novel material,
given a set or stream of documents on a certain topic. This study investigates the more …

In many cases, rather than a keyword search, people intend to see what is going on through
the Internet. Then the integrated comprehensive information on news topics is necessary …

News topics, which are constructed from news stories using the techniques of Topic
Detection and Tracking (TDT), bring convenience to users who intend to see what is going …

In this paper, an algorithm called Time Driven Documents-partition (TDD) is proposed to
construct an event hierarchy in a text corpus based on a given query. Specifically, assume …

Traditional personalized search approaches rely solely on individual profiles to construct a
user model. They are often confronted by two major problems: data sparseness and cold …

This paper describes an approach to text classification using language models. This
approach is a natural extension of the traditional Naïve Bayes classifier, in which we replace …

News Topics are related to a set of keywords or keyphrases. Topic keyphrases briefly
describe the key content of topics and help users decide whether to do further reading about …