ECNU: Effective semantic relations classification without complicated features or multiple external corpora

Authors:
Yuan Chen;Man Lan;Jian Su;Zhi Min Zhou;Yu Xu
Affiliations:
East China Normal University, Shanghai, PRC;East China Normal University, Shanghai, PRC and Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;East China Normal University, Shanghai, PRC;East China Normal University, Shanghai, PRC
Venue:
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Year:
2010

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Abstract

This paper describes our approach to the automatic identification of semantic relations between nominals in English sentences. The basic idea of our strategy is to develop machine-learning classifiers which: (1) make use of class-independent features and classifier; (2) make use of a simple and effective feature set without high computational cost; (3) make no use of external annotated or unannotated corpus at all. At SemEval 2010 Task 8 our system achieved an F-measure of 75.43% and a accuracy of 70.22%.