Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Enriching the output of a parser using memory-based learning
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Information Sciences: an International Journal
A multi-phase approach to biomedical event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Investigating statistical techniques for sentence-level event classification
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Language specific issue and feature exploration in Chinese event extraction
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Can one language bootstrap the other: a case study on event extraction
SemiSupLearn '09 Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing
UMSLLS '09 Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics
Self-similarity Clustering Event Detection Based on Triggers Guidance
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
Sentence-level event classification in unstructured texts
Information Retrieval
Using document level cross-event inference to improve event extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Unsupervised event coreference resolution with rich linguistic features
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
A pairwise event coreference model, feature impact and evaluation for event coreference resolution
eETTs '09 Proceedings of the Workshop on Events in Emerging Text Types
ID 392: TERSEO + T2T3 Transducer: a systems for recognizing and normalizing TIMEX3
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Extracting 5W1H event semantic elements from Chinese online news
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Event detection using lexical chain
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Using cross-entity inference to improve event extraction
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automatic transformation from TIDES to TimeML annotation
Language Resources and Evaluation
Chinese news event 5W1H semantic elements extraction for event ontology population
Proceedings of the 21st international conference companion on World Wide Web
Event-centric search and exploration in document collections
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Employing compositional semantics and discourse consistency in Chinese event extraction
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Event argument extraction based on CRF
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Exploring coreference uncertainty of generically extracted event mentions
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
Using compositional semantics and discourse consistency to improve Chinese trigger identification
Information Processing and Management: an International Journal
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Event detection and recognition is a complex task consisting of multiple sub-tasks of varying difficulty. In this paper, we present a simple, modular approach to event extraction that allows us to experiment with a variety of machine learning methods for these sub-tasks, as well as to evaluate the impact on performance these sub-tasks have on the overall task.