Towards a general theory of action and time
Artificial Intelligence
DIRT @SBT@discovery of inference rules from text
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Unsupervised word sense disambiguation rivaling supervised methods
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Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
The unsupervised learning of natural language structure
The unsupervised learning of natural language structure
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
A composite kernel to extract relations between entities with both flat and structured features
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Evita: a robust event recognizer for QA systems
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Acquisition of verb entailment from text
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Timelines from Text: Identification of Syntactic Temporal Relations
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Finding event, temporal and causal structure in text: a machine learning approach
Finding event, temporal and causal structure in text: a machine learning approach
Learning semantic links from a corpus of parallel temporal and causal relations
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Classifying temporal relations between events
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
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COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EM works for pronoun anaphora resolution
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Unsupervised models for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Jointly combining implicit constraints improves temporal ordering
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SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
CU-TMP: temporal relation classification using syntactic and semantic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
LCC-TE: a hybrid approach to temporal relation identification in news text
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NAIST.Japan: temporal relation identification using dependency parsed tree
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
USFD: preliminary exploration of features and classifiers for the TempEval-2007 tasks
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
WVALI: temporal relation identification by syntactico-semantic analysis
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
XRCE-T: XIP temporal module for TempEval campaign
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
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Journal of Artificial Intelligence Research
TimeML-compliant text analysis for temporal reasoning
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Jointly identifying temporal relations with Markov Logic
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
An expectation maximization approach to pronoun resolution
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
SemEval-2010 task 13: TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TRIPS and TRIOS system for TempEval-2: Extracting temporal information from text
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
TIPSem (English and Spanish): Evaluating CRFs and semantic roles in TempEval-2
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
USFD2: Annotating temporal expresions and TLINKs for TempEval-2
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NCSU: Modeling temporal relations with Markov logic and lexical ontology
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JU_CSE_TEMP: A first step towards evaluating events, time expressions and temporal relations
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COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Journal of Computer Science and Technology - Special issue on natural language processing
Semisupervised condensed nearest neighbor for part-of-speech tagging
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Global path-based refinement of noisy graphs applied to verb semantics
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as Question Answering, Information Extraction, and Summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, we have concentrated our efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, inspired by the hypothesis of "one type of temporal relation per discourse", it extracts useful information from a cluster of topically related documents. We show that by combining the global information of such a cluster with local decisions of a general classifier, a bootstrapping cross-document classifier can be built to extract temporal relations between events. Our experiments show that without any additional annotated data, the accuracy of the proposed algorithm is higher than that of several previous successful systems. The second proposed method for temporal relation extraction is based on the expectation maximization (EM) algorithm. Within EM, we used different techniques such as a greedy best-first search and integer linear programming for temporal inconsistency removal. We think that the experimental results of our EM based algorithm, as a first step toward a fully unsupervised temporal relation extraction method, is encouraging.