Applying machine learning to Chinese temporal relation resolution

Authors:
Wenjie Li;Kam-Fai Wong;Guihong Cao;Chunfa Yuan
Affiliations:
The Hong Kong Polytechnic University, Hong Kong;The Chinese University of Hong Kong, Hong Kong;The Hong Kong Polytechnic University, Hong Kong;Tsinghua University, Beijing, China.
Venue:
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Year:
2004

Citing 11
Cited 9

A Distance-Based Attribute Selection Measure for Decision Tree Induction

Machine Learning
A Statistical-Heuristic Feature Selection Criterion for Decision Tree Induction

IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning

C4.5: programs for machine learning
Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A Machine Learning Approach to POS Tagging

Machine Learning
A word-based approach for modeling and discovering temporal relations embedded in Chinese sentences

ACM Transactions on Asian Language Information Processing (TALIP)
Learning methods to combine linguistic indicators: improving aspectual classification and revealing linguistic insights

Computational Linguistics
Algorithms for analysing the temporal structure of discourse

EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
Decision lists for lexical ambiguity resolution: application to accent restoration in Spanish and French

ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Inferring discourse relations in context

ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
An empirical approach to temporal reference resolution

Journal of Artificial Intelligence Research

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
Multidocument Summary Generation: Using Informative and Event Words

ACM Transactions on Asian Language Information Processing (TALIP)
Extractive summarization using supervised and semi-supervised learning

COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A pilot study on acquiring metric temporal constraints for events

ARTE '06 Proceedings of the Workshop on Annotating and Reasoning about Time and Events
Comparison of different algebras for inducing the temporal structure of texts

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Automatic temporal expression normalization with reference time dynamic-choosing

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Data-driven approach based on semantic roles for recognizing temporal expressions and events in Chinese

NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Tense tagging for verbs in cross-lingual context: a case study

IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Exploiting temporal information in Web search

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Temporal relation resolution involves extraction of temporal information explicitly or implicitly embedded in a language. This information is often inferred from a variety of interactive grammatical and lexical cues, especially in Chinese. For this purpose, inter-clause relations (temporal or otherwise) in a multiple-clause sentence play an important role. In this paper, a computational model based on machine learning and heterogeneous collaborative bootstrapping is proposed for analyzing temporal relations in a Chinese multiple-clause sentence. The model makes use of the fact that events are represented in different temporal structures. It takes into account the effects of linguistic features such as tense/aspect, temporal connectives, and discourse structures. A set of experiments has been conducted to investigate how linguistic features could affect temporal relation resolution.