Exploring the effectiveness of lexical ontologies for modeling temporal relations with Markov logic

Authors:
Eun Y. Ha;Alok Baikadi;Carlyle J. Licata;Bradford W. Mott;James C. Lester
Affiliations:
North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC;North Carolina State University, Raleigh, NC
Venue:
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Year:
2010

Citing 10
Cited 0

A maximum-entropy-inspired parser

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Markov logic networks

Machine Learning
NLTK: the Natural Language Toolkit

ETMTNLP '02 Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics - Volume 1
Jointly combining implicit constraints improves temporal ordering

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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
SemEval-2010 task 13: evaluating events, time expressions, and temporal relations (TempEval-2)

DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
TimeML-compliant text analysis for temporal reasoning

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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
NCSU: Modeling temporal relations with Markov logic and lexical ontology

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Temporal analysis of events is a central problem in computational models of discourse. However, correctly recognizing temporal aspects of events poses serious challenges. This paper introduces a joint modeling framework and feature set for temporal analysis of events that utilizes Markov Logic. The feature set includes novel features derived from lexical ontologies. An evaluation suggests that introducing lexical relation features improves the overall accuracy of temporal relation models.