Feature distance-based framework for classification of low-frequency semantic relations

André Kenji Horie, Mitsuru Ishizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In the relation extraction of semantic relations, it is not uncommon to face settings in which the training data provides very few instances of some relation classes. This is mostly due to the high cost of producing such data and to the class imbalance problem, which may result in some classes presenting small frequencies even with a large annotated corpus. This work thus presents a semi-supervised bootstrapped method to expand this initial training dataset, using pattern matching to extract new candidate instances from the Web. The core of this process uses a multiview feature distance-based framework, which allows quantitative and qualitative analysis of intermediate steps of the process. Experimental results show that this framework provides better results in the relation classification task than the baseline, and the bootstrapped architecture improves the relation classification task as a whole for these low-frequency semantic relations settings.

Original languageEnglish
Title of host publicationProceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011
Pages59-66
Number of pages8
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event5th Annual IEEE International Conference on Semantic Computing, ICSC 2011 - Palo Alto, CA
Duration: 2011 Sep 182011 Sep 21

Other

Other5th Annual IEEE International Conference on Semantic Computing, ICSC 2011
CityPalo Alto, CA
Period11/9/1811/9/21

Fingerprint

Low Frequency
Semantics
Pattern matching
Costs
Pattern Matching
Qualitative Analysis
Quantitative Analysis
Expand
Framework
Baseline
Experimental Results
Class
Training

Keywords

  • Concept description
  • Natural language text
  • Semantic computing

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

Cite this

Horie, A. K., & Ishizuka, M. (2011). Feature distance-based framework for classification of low-frequency semantic relations. In Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011 (pp. 59-66). [6061437] https://doi.org/10.1109/ICSC.2011.9

Feature distance-based framework for classification of low-frequency semantic relations. / Horie, André Kenji; Ishizuka, Mitsuru.

Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011. 2011. p. 59-66 6061437.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Horie, AK & Ishizuka, M 2011, Feature distance-based framework for classification of low-frequency semantic relations. in Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011., 6061437, pp. 59-66, 5th Annual IEEE International Conference on Semantic Computing, ICSC 2011, Palo Alto, CA, 11/9/18. https://doi.org/10.1109/ICSC.2011.9
Horie AK, Ishizuka M. Feature distance-based framework for classification of low-frequency semantic relations. In Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011. 2011. p. 59-66. 6061437 https://doi.org/10.1109/ICSC.2011.9
Horie, André Kenji ; Ishizuka, Mitsuru. / Feature distance-based framework for classification of low-frequency semantic relations. Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011. 2011. pp. 59-66
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