The nature of statistical learning theory
The nature of statistical learning theory
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Assigning time-stamps to event-clauses
TASIP '01 Proceedings of the workshop on Temporal and spatial information processing - Volume 13
A study on convolution kernels for shallow semantic parsing
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Learning event durations from event descriptions
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning by reading: a prototype system, performance baseline and lessons learned
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Kernel methods for minimally supervised wsd
Computational Linguistics
SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Machine reading as a process of partial question-answering
FAM-LbR '10 Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading
Syntactic tree kernels for event-time temporal relation learning
LTC'09 Proceedings of the 4th conference on Human language technology: challenges for computer science and linguistics
Identifying constant and unique relations by using time-series text
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Summaries on the fly: query-based extraction of structured knowledge from web documents
ICWE'13 Proceedings of the 13th international conference on Web Engineering
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We present work on linking events and fluents (i.e., relations that hold for certain periods of time) to temporal information in text, which is an important enabler for many applications such as timelines and reasoning. Previous research has mainly focused on temporal links for events, and we extend that work to include fluents as well, presenting a common methodology for linking both events and relations to timestamps within the same sentence. Our approach combines tree kernels with classical feature-based learning to exploit context and achieves competitive F1-scores on event-time linking, and comparable F1-scores for fluents. Our best systems achieve F1-scores of 0.76 on events and 0.72 on fluents.