Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic acquisition of domain knowledge for Information Extraction
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Counter-training in discovery of semantic patterns
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
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
A semantic approach to IE pattern induction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Names and similarities on the web: fact extraction in the fast lane
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
An empirical approach to temporal reference resolution
Journal of Artificial Intelligence Research
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Automatic time expression labeling for english and chinese text
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Model-portability experiments for textual temporal analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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We present a semi-supervised (bootstrapping) approach to the extraction of time expression mentions in large unlabelled corpora. Because the only supervision is in the form of seed examples, it becomes necessary to resort to heuristics to rank and filter out spurious patterns and candidate time expressions. The application of bootstrapping to time expression recognition is, to the best of our knowledge, novel. In this paper, we describe one such architecture for bootstrapping Information Extraction (IE) patterns ---suited to the extraction of entities, as opposed to events or relations--- and summarize our experimental findings. These point out to the fact that a pattern set with a good increase in recall with respect to the seeds is achievable within our framework while, on the other side, the decrease in precision in successive iterations is succesfully controlled through the use of ranking and selection heuristics. Experiments are still underway to achieve the best use of these heuristics and other parameters of the bootstrapping algorithm.