Improving chinese event construction extraction with lexical relation pairs

Authors:
Liou Chen;Qiang Zhou;Hongxian Wang
Affiliations:
Department of Computer Science and Technology, Tsinghua University, China;National Laboratory for Information Science and Technology, Tsinghua University, China;Department of Computer Science and Technology, Tsinghua University, China
Venue:
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Year:
2012

Citing 6
Cited 0

Labeling chinese predicates with semantic roles

Computational Linguistics
Fast Semantic Role Labeling for Chinese Based on Semantic Chunking

ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Chinese semantic role labeling with shallow parsing

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Semantics-driven shallow parsing for Chinese semantic role labeling

ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
SemEval-2010 task 11: Event detection in Chinese news sentences

SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Chinese Semantic Role Labeling with Hierarchical Semantic Knowledge

ICECE '10 Proceedings of the 2010 International Conference on Electrical and Control Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Semantic Role Labeling (SRL) has proved to be a valuable tool for performing automatic analysis of natural language text. SRL requires additional information besides lexical and syntactical features to recognize the candidate arguments better. This paper investigates the use of Lexical Relation Pair (LRP) information for the task of Event Construction Extraction. Experimental results show that introducing LRP features can achieve some improvements. Further experiments indicate that using automatically extracted LRPs can obtain even better performance than manually annotated ones. This observation enables the large-scale usage of LRPs in the future.