Supporting News Article Understanding by Detecting Subject-Background Event Relations

• Published in: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) pp. 256 - 263
• Main Authors: Tanaka, Shotaro, Jatowt, Adam, Tanaka, Katsumi
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.10.2016
• Subjects: Context; Feature extraction; Frequency measurement; Information retrieval; Strips; Support vector machines; Visualization
• DOI: 10.1109/WI.2016.0044

Typically, news articles mention not just one but multiple events. These events can be classified into subject or background events. The former are events that the article is written about, while the latter are additional events referred to in order to explain the background of the subject events (e.g., causal relations, circumstances or the consequences of the main event). Background events are considered to play an important role in helping to understand articles. In this paper, we first propose to classify content of news articles into subject or background event descriptions. In the second part of the paper, we demonstrate a novel solution for improving the news article search. Based on the subject and background relationship structure between events and articles, our method outputs news articles that help with understanding of a given target article.