Making large-scale support vector machine learning practical
Advances in kernel methods
The Frame-Based Module of the SUISEKI Information Extraction System
IEEE Intelligent Systems
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PathwayFinder: paving the way towards automatic pathway extraction
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
An improved extraction pattern representation model for automatic IE pattern acquisition
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Dependency tree kernels for relation extraction
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Collective information extraction with relational Markov networks
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Extracting relations with integrated information using kernel methods
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd 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
Feature forest models for probabilistic hpsg parsing
Computational Linguistics
The role of syntactic features in protein interaction extraction
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
Journal of Biomedical Informatics
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Procedural knowledge extraction on MEDLINE abstracts
AMT'11 Proceedings of the 7th international conference on Active media technology
Feasibility study for procedural knowledge extraction in biomedical documents
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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This paper presents a method of automatically constructing information extraction patterns on predicate-argument structures (PASs) obtained by full parsing from a smaller training corpus. Because PASs represent generalized structures for syntactical variants, patterns on PASs are expected to be more generalized than those on surface words. In addition, patterns are divided into components to improve recall and we introduce a Support Vector Machine to learn a prediction model using pattern matching results. In this paper, we present experimental results and analyze them on how well protein-protein interactions were extracted from MEDLINE abstracts. The results demonstrated that our method improved accuracy compared to a machine learning approach using surface word/part-of-speech patterns.