An algorithm for suffix stripping
Readings in information retrieval
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Matching Hierarchical Structures Using Association Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Algorithms
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Searching Substructures with Superimposed Distance
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Text Mining for Biology And Biomedicine
Text Mining for Biology And Biomedicine
SAGA: a subgraph matching tool for biological graphs
Bioinformatics
Data & Knowledge Engineering
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
Event extraction from trimmed dependency graphs
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Syntactic dependency based heuristics for biological event extraction
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task
Statistical anaphora resolution in biomedical texts
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Overview of BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Overview of Genia event task in BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Overview of Genia event task in BioNLP Shared Task 2011
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
New resources and perspectives for biomedical event extraction
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
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We participated in the BioNLP Shared Task 2011, addressing the GENIA event extraction (GE) and the Epigenetics and Post-translational Modifications (EPI) tasks. A graph-based approach is employed to automatically learn rules for detecting biological events in the life-science literature. The event rules are learned by identifying the key contextual dependencies from full syntactic parsing of annotated text. Event recognition is performed by searching for an isomorphism between event rules and the dependency graphs of sentences in the input texts. While we explored methods such as performance-based rule ranking to improve precision, we merged rules across multiple event types in order to increase recall. We achieved a 41.13% F-score in detecting events of nine types in the Task 1 of the GE task, and a 52.67% F-score in identifying events across fifteen types in the core task of the EPI task. Our performance on both tasks is comparable to the state-of-the-art systems. Our approach does not require any external domain-specific resources. The consistent performance on the two tasks supports the claim that the method generalizes well to extract events from different domains where training data is available.