Towards a general theory of action and time
Artificial Intelligence
Temporal ontology and temporal reference
Computational Linguistics - Special issue on tense and aspect
A computational model of the semantics of tense and aspect
Computational Linguistics - Special issue on tense and aspect
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Temporal representation and reasoning in artificial intelligence: Issues and approaches
Annals of Mathematics and Artificial Intelligence
Automatic labeling of semantic roles
Computational Linguistics
A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Algorithms for analysing the temporal structure of discourse
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Learning parse and translation decisions from examples with rich context
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
The kappa statistic: a second look
Computational Linguistics
A framework for resolution of time in natural language
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on Temporal Information Processing
An ontology of time for the semantic web
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on Temporal Information Processing
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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
Journal of Biomedical Informatics
Learning sentence-internal temporal relations
Journal of Artificial Intelligence Research
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Possibilistic temporal reasoning based on fuzzy temporal constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Unsupervised event coreference resolution with rich linguistic features
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Time-oriented question answering from clinical narratives sing semantic-web techniques
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Jobshop scheduling with imprecise durations: a fuzzy approach
IEEE Transactions on Fuzzy Systems
Extracting fine-grained durations for verbs from Twitter
ACL '12 Proceedings of ACL 2012 Student Research Workshop
Extracting and modeling durations for habits and events from Twitter
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Computational Linguistics
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This article presents our work on constructing a corpus of news articles in which events are annotated for estimated bounds on their duration, and automatically learning from this corpus. We describe the annotation guidelines, the event classes we categorized to reduce gross discrepancies in inter-annotator judgments, and our use of normal distributions to model vague and implicit temporal information and to measure inter-annotator agreement for these event duration distributions. We then show that machine learning techniques applied to this data can produce coarse-grained event duration information automatically, considerably outperforming a baseline and approaching human performance. The methods described here should be applicable to other kinds of vague but substantive information in texts.