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Title: Topic tracking with time granularity reasoning
Authors: Li, B
Li, W 
Lu, Q 
Keywords: Event tracking
Time granularity
Time reasoning
Topic detection and tracking
Topic tracking
Issue Date: 2006
Source: ACM transactions on Asian language information processing, 2006, v. 5, no. 4, p. 388-412 How to cite?
Journal: ACM Transactions on Asian Language Information Processing 
Abstract: Temporal information is an important attribute of a topic, and a topic usually exists in a limited period. Therefore, many researchers have explored the utilization of temporal information in topic detection and tracking (TDT). They use either a story's publication time or temporal expressions in text to derive temporal relatedness between two stories or a story and a topic. However, past research neglects the fact that people tend to express a time with different granularities as time lapses. Based on a careful investigation of temporal information in news streams, we propose a new strategy with time granularity reasoning for utilizing temporal information in topic tracking. A set of topic times, which as a whole represent the temporal attribute of a topic, are distinguished from others in the given on-topic stories. The temporal relatedness between a story and a topic is then determined by the highest coreference level between each time in the story and each topic time where the coreference level between a test time and a topic time is inferred from the two times themselves, their granularities, and the time distance between the topic time and the publication time of the story where the test time appears. Furthermore, the similarity value between an incoming story and a topic, that is the likelihood that a story is on-topic, can be adjusted only when the new story is both temporally and semantically related to the target topic. Experiments on two different TDT corpora show that our proposed method could make good use of temporal information in news stories, and it consistently outperforms the baseline centroid algorithm and other algorithms which consider temporal relatedness.
ISSN: 1530-0226
DOI: 10.1145/1236181.1236186
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