An investigation of linguistic features and clustering algorithms for topical document clustering
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
Inferring temporal ordering of events in news
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
Robust temporal processing of news
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Assigning time-stamps to event-clauses
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
Syntactic simplification for improving content selection in multi-document summarization
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Predicting unknown time arguments based on cross-event propagation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Challenges from information extraction to information fusion
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Proceedings of the 17th ACM international conference on Supporting group work
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Many applications in NLP, such as question-answering and summarization, either require or would greatly benefit from the knowledge of when an event occurred. Creating an effective algorithm for identifying the activity time of an event in news is difficult in part because of the sparsity of explicit temporal expressions. This paper describes a domain-independent machine-learning based approach to assign activity times to events in news. We demonstrate that by applying topic models to text, we are able to cluster sentences that describe the same event, and utilize the temporal information within these event clusters to infer activity times for all sentences. Experimental evidence suggests that this is a promising approach, given evaluations performed on three distinct news article sets against the baseline of assigning the publication date. Our approach achieves 90%, 88.7%, and 68.7% accuracy, respectively, outperforming the baseline twice.