A study on dependency tree kernels for automatic extraction of protein-protein interaction

Authors:
Faisal Mahbub Chowdhury;Alberto Lavelli;Alessandro Moschitti
Affiliations:
University of Trento, Italy;Human Language Technology Research Unit, Fondazione Bruno Kessler, Trento, Italy;University of Trento, Italy
Venue:
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Year:
2011

Citing 10
Cited 2

Making large-scale support vector machine learning practical

Advances in kernel methods
Text classification using string kernels

The Journal of Machine Learning Research
A study on convolution kernels for shallow semantic parsing

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Dependency tree kernels for relation extraction

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A shortest path dependency kernel for relation extraction

HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A graph kernel for protein-protein interaction extraction

BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Comparative experiments on learning information extractors for proteins and their interactions

Artificial Intelligence in Medicine
Convolution kernels on constituent, dependency and sequential structures for relation extraction

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Efficient convolution kernels for dependency and constituent syntactic trees

ECML'06 Proceedings of the 17th European conference on Machine Learning
Discovering relations between named entities from a large raw corpus using tree similarity-based clustering

IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing

Tree kernel-based protein-protein interaction extraction from biomedical literature

Journal of Biomedical Informatics
Combining tree structures, flat features and patterns for biomedical relation extraction

EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Kernel methods are considered the most effective techniques for various relation extraction (RE) tasks as they provide higher accuracy than other approaches. In this paper, we introduce new dependency tree (DT) kernels for RE by improving on previously proposed dependency tree structures. These are further enhanced to design more effective approaches that we call mildly extended dependency tree (MEDT) kernels. The empirical results on the protein-protein interaction (PPI) extraction task on the AIMed corpus show that tree kernels based on our proposed DT structures achieve higher accuracy than previously proposed DT and phrase structure tree (PST) kernels.