A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
Constraint-based event recognition for information extraction
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Computational aspects of discourse in the context of MUC-3
MUC3 '91 Proceedings of the 3rd conference on Message understanding
Assigning time-stamps to event-clauses
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
Machine learning of temporal relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Incremental topic representations
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Infrastructure for open-domain information extraction
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Exploiting structure for event discovery using the MDI algorithm
ACL '07 Proceedings of the 45th Annual Meeting of the ACL: Student Research Workshop
Pattern matching and discourse processing in information extraction from Japanese text
Journal of Artificial Intelligence Research
Wrap-Up: a trainable discourse module for information extraction
Journal of Artificial Intelligence Research
Predicting unknown time arguments based on cross-event propagation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Filtering and clustering relations for unsupervised information extraction in open domain
Proceedings of the 20th ACM international conference on Information and knowledge management
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One of the early application of Information Extraction, motivated by the needs for intelligence tools, is the detection of events in news articles. But this detection may be difficult when news articles mention several occurrences of events of the same kind, which is often done for comparison purposes. We propose in this article new approaches to segment the text of news articles in units relative to only one event, in order to help the identification of relevant information associated with the main event of the news. We present two approaches that use statistical machine learning models (HMM and CRF) exploiting temporal information extracted from the texts as a basis for this segmentation. The evaluation of these approaches in the domain of seismic events show that with a robust and generic approach, we can achieve results at least as good as results obtained with a specialized heuristic approach.