Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A vector space model for automatic indexing
Communications of the ACM
Temporal Granularity: Completing the Puzzle
Journal of Intelligent Information Systems
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Merging structured text using temporal knowledge
Data & Knowledge Engineering
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A System for new event detection
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Simple Semantics in Topic Detection and Tracking
Information Retrieval
Text classification and named entities for new event detection
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Language-specific models in multilingual topic tracking
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in 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
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Representing and Reasoning about Temporal Granularities
Journal of Logic and Computation
Topic tracking based on keywords dependency profile
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
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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.