UTH: SVM-based semantic relation classification using physical sizes

Authors:
Eiji Aramaki;Takeshi Imai;Kengo Miyo;Kazuhiko Ohe
Affiliations:
The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan;The University of Tokyo, Bunkyo-ku, Tokyo, Japan
Venue:
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Year:
2007

Citing 8
Cited 2

Simplifying decision trees

International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
The nature of statistical learning theory

The nature of statistical learning theory
Extracting Patterns and Relations from the World Wide Web

WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Automatic acquisition of hyponyms from large text corpora

COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding parts in very large corpora

ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Interpreting semantic relations in noun compounds via verb semantics

COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Models for the semantic classification of noun phrases

CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Measuring semantic similarity by latent relational analysis

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence

A knowledge-rich approach to identifying semantic relations between nominals

Information Processing and Management: an International Journal
Extraction and approximation of numerical attributes from the Web

ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Although researchers have shown increasing interest in extracting/classifying semantic relations, most previous studies have basically relied on lexical patterns between terms. This paper proposes a novel way to accomplish the task: a system that captures a physical size of an entity. Experimental results revealed that our proposed method is feasible and prevents the problems inherent in other methods.