In this paper we describe a way to discover Named Entities by using the distribution of words in news articles. Named Entity recognition is an important task for today’s natural language applications, but it still suffers for its data sparseness. We used an observation that a Named Entity often appears synchronously in several news articles, whereas a common noun doesn’t. Exploiting this charac...

The rapid growth of Internet has revolutionized online news reporting. Many users tend to use online news websites to obtain news information. When considering Sri Lanka, there are numerous news websites, which are subscribed on a daily basis. With the rise in this number of news websites, the Sri Lankan authorities of media face the issue of lacking a proper methodology or a tool which is capa...

In this paper we present the CoNews framework, a hybrid approach to publish online news stories, consisting of the combination of news entries extracted from authoritative sources retrieved from news search engines with blog articles and user-submitted less-authoritative stories. Some of the goals of this research include experimenting with new forms of mass communication and editorial control ...

When was the last time you read a newspaper and was bombarded with articles you would rather not see? Current news media shows massive number of news every day. But from tragedies to happy stories, people might want to choose to read only those articles that fit their current mood. The purpose of this project is to present the Magnet News, a Web tool in which users can choose if they want to se...

We are developing an Intelligent Network News Reader which extracts news articles for users. In contrast to ordinary information retrieval and abstract generation, this method utilizes an "informa-tion context" to select articles from newsgroups on the Internet and it displays the context visually. A salient feature of this system is that it retrieves articles dynamically, adapting itself to th...

Disagreement among text annotators as a part of human (expert) labeling process produces noisy labels, which affect the performance supervised learning algorithms for natural language processing. Using only high agreement annotations introduces another challenge: data imbalance problem. We study this challenge within problem relating user comments to content news article. show that traditional ...

We address the problem of filtering medical news articles for targeted audiences. The approach is based on terms and one of the difficulties is extracting a feature set appropriate for the domain. This paper addresses the medical news-filtering problem using a machine learning approach. We describe the application of two supervised machine learning techniques, Decision Trees and Naïve Bayes, to...