Vectorization of Text Documents for Identifying Unifiable News Articles

Identifying Political Bias in News Articles

Identifying archetypal perspectives in news articles

A novel approach to news aggregation is proposed. Rather than ranking or summarisation of cluster topics, we propose that articles are grouped by topic similarity and then clustered within topic groups in order to identify archetypal articles that represent the various perspectives upon a topic. An example application is examined and a preliminary user study is discussed. Future applications an...

"Making the News": Identifying Noteworthy Events in News Articles

Most events described in a news article are background events – only a small number are noteworthy, and a even smaller number serve as the trigger for writing of that article. Although these events are difficult to identify, they are crucial to NLP tasks such as first story detection, document summarization and event coreference, and to many applications of event analysis that depend on event c...

Ontology-based Text Summarization for Business News Articles

In this paper, we compare two methods for article summarization. The first method is mainly based on term-frequency, while the second method is based on ontology. We build an ontology database for analyzing the main topics of the article. After identifying the main topics and determining their relative significance, we rank the paragraphs based on the relevance between main topics and each indi...

Arabic News Articles Classification Using Vectorized-Cosine Based on Seed Documents

Besides for its own merits, text classification (TC) has become a cornerstone in many applications. Work presented here is part of and a pre-requisite for a project we have overtaken to create a corpus for the Arabic text process. It is an attempt to create modules automatically that would help speed up the process of classification for any text categorization task. It also serves as a tool for...