Original scientific paper
https://doi.org/10.2498/cit.1001104
Applying Dynamic Co-occurrence in Story Link Detection
Hua Zhao
Tiejun Zhao
Abstract
Story link detection is part of a broader initiative called Topic
Detection and Tracking, which is defined to be the task of
determining whether two stories, such as news articles or radio
broadcasts, are about the same event, or linked. In order to mine
more information from the contents of the stories being compared and
achieve a more high-powered system, motivated by the idea of the
word co-occurrence analysis, we propose our dynamic co-occurrence,
which is defined to be a pair of words that satisfy certain relation
restriction. In this paper, relation restriction refers to a set of
features. This paper evaluates three features: capital, location and
distance. We use dynamic co-occurrence in the similarity computation
when we apply it in the story link detection system. Experimental
results show that the story link detection systems based on the
dynamic co-occurrence perform very well, which testify the great
capabilities of the dynamic co-occurrence. At the same time, we also
find that relation restriction is critical to the performance of
dynamic co-occurrence.
Keywords
Hrčak ID:
44855
URI
Publication date:
30.6.2009.
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