High-precision biological event extraction with a concept recognizer
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Molecular event extraction from link grammar parse trees
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Recognising the predicate-argument structure of Tagalog
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Correlating natural language parser performance with statistical measures of the text
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Extracting Protein Interactions from Text with the Unified AkaneRE Event Extraction System
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient Extraction of Protein-Protein Interactions from Full-Text Articles
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A comparative study of syntactic parsers for event extraction
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
Evaluating dependency representation for event extraction
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multiple kernel learning in protein-protein interaction extraction from biomedical literature
Artificial Intelligence in Medicine
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Dependency graphs as a generic interface between parsers and relation extraction rule learning
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
From graphs to events: a subgraph matching approach for information extraction from biomedical text
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Fast and robust joint models for biomedical event extraction
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Tree kernel-based protein-protein interaction extraction from biomedical literature
Journal of Biomedical Informatics
Journal of Biomedical Informatics
High precision rule based PPI extraction and per-pair basis performance evaluation
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CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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Motivation: While text mining technologies for biomedical research have gained popularity as a way to take advantage of the explosive growth of information in text form in biomedical papers, selecting appropriate natural language processing (NLP) tools is still difficult for researchers who are not familiar with recent advances in NLP. This article provides a comparative evaluation of several state-of-the-art natural language parsers, focusing on the task of extracting protein–protein interaction (PPI) from biomedical papers. We measure how each parser, and its output representation, contributes to accuracy improvement when the parser is used as a component in a PPI system. Results: All the parsers attained improvements in accuracy of PPI extraction. The levels of accuracy obtained with these different parsers vary slightly, while differences in parsing speed are larger. The best accuracy in this work was obtained when we combined Miyao and Tsujii's Enju parser and Charniak and Johnson's reranking parser, and the accuracy is better than the state-of-the-art results on the same data. Availability: The PPI extraction system used in this work (AkanePPI) is available online at http://www-tsujii.is.s.u-tokyo.ac.jp/-100downloads/downloads.cgi. The evaluated parsers are also available online from each developer's site. Contact: yusuke@is.s.u-tokyo.ac.jp