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Judgment about the Usefulness of Automatically Extracted Temporal Information from News Articles for Event Detection and Tracking  

Kim Pyung (한국과학기술정보연구원 NTIS 사업단)
Myaeng Sung-Hyon (한국정보통신대학교 공학부)
Publication Information
Journal of KIISE:Software and Applications / v.33, no.6, 2006 , pp. 564-573 More about this Journal
Abstract
Temporal information plays an important role in natural language processing (NLP) applications such as information extraction, discourse analysis, automatic summarization, and question-answering. In the topic detection and tracking (TDT) area, the temporal information often used is the publication date of a message, which is readily available but limited in its usefulness. We developed a relatively simple NLP method of extracting temporal information from Korean news articles, with the goal of improving performance of TDT tasks. To extract temporal information, we make use of finite state automata and a lexicon containing time-revealing vocabulary. Extracted information is converted into a canonicalized representation of a time point or a time duration. We first evaluated the extraction and canonicalization methods for their accuracy and investigated on the extent to which temporal information extracted as such can help TDT tasks. The experimental results show that time information extracted from text indeed helps improve both precision and recall significantly.
Keywords
temporal information extracting; event detection and tracking;
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