Simple Semantics in Topic Detection and Tracking
Information Retrieval
Usefulness of temporal information automatically extracted from news articles for topic tracking
ACM Transactions on Asian Language Information Processing (TALIP)
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
News Recommender System Based on Topic Detection and Tracking
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
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In this paper, we propose a new strategy with time granularity reasoning for utilizing temporal information in topic tracking. Compared with previous ones, our work has four distinguished characteristics. Firstly, we try to determine a set of topic times for a target topic from the given on-topic stories. It helps to avoid the negative influence from other irrelevant times. Secondly, we take into account time granularity variance when deciding whether a coreference relationship exists between two times. Thirdly, both publication time and times presented in texts are considered. Finally, as time is only one attribute of a topic, we increase the similarity between a story and a target topic only when they are related not only temporally but also semantically. Experiments on two TDT corpora show that our method makes good use of temporal information in news stories.