Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Natural Language Engineering
RelEx---Relation extraction using dependency parse trees
Bioinformatics
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
The role of syntactic features in protein interaction extraction
Proceedings of the 2nd international workshop on Data and text mining in bioinformatics
High-performance information extraction with AliBaba
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Overview of BioNLP'09 shared task on event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
From protein-protein interaction to molecular event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Bioinformatics
An annotation type system for a data-driven NLP pipeline
LAW '07 Proceedings of the Linguistic Annotation Workshop
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
A rich feature vector for protein-protein interaction extraction from multiple corpora
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
An Overview of BioCreative II.5
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Adapting a probabilistic disambiguation model of an HPSG parser to a new domain
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
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Currently, relation extraction (RE) and event extraction (EE) are the two main streams of biological information extraction. In 2009, the majority of these RE and EE research efforts were centered around the BioCreative II.5 Protein-Protein Interaction (PPI) challenge and the “BioNLP event extraction shared task.” Although these challenges took somewhat different approaches, they share the same ultimate goal of extracting bio-knowledge from the literature. This paper compares the two challenge task definitions, and presents a unified system that was successfully applied in both these and several other PPI extraction task settings. The AkaneRE system has three parts: A core engine for RE, a pool of modules for specific solutions, and a configuration language to adapt the system to different tasks. The core engine is based on machine learning, using either Support Vector Machines or Statistical Classifiers and features extracted from given training data. The specific modules solve tasks like sentence boundary detection, tokenization, stemming, part-of-speech tagging, parsing, named entity recognition, generation of potential relations, generation of machine learning features for each relation, and finally, assignment of confidence scores and ranking of candidate relations. With these components, the AkaneRE system produces state-of-the-art results, and the system is freely available for academic purposes at http://www-tsujii.is.s.u-tokyo.ac.jp/satre/akane/.