A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Extracting Biochemical Interactions from MEDLINE Using a Link Grammar Parser
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
A dependency-based method for evaluating broad-coverage parsers
Natural Language Engineering
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Event-based information extraction for the biomedical domain: the Caderige project
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
IJCNLP'05 Proceedings of the Second international joint conference on 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
The Stanford typed dependencies representation
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Toward an underspecifiable corpus annotation scheme
CrossParser '08 Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation
Adapting a lexicalized-grammar parser to contrasting domains
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Towards effective sentence simplification for automatic processing of biomedical text
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
Evaluating the effects of treebank size in a practical application for parsing
SETQA-NLP '08 Software Engineering, Testing, and Quality Assurance for Natural Language Processing
Porting a lexicalized-grammar parser to the biomedical domain
Journal of Biomedical Informatics
Faster parsing by supertagger adaptation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
Computer Methods and Programs in Biomedicine
Hi-index | 0.01 |
Several incompatible syntactic annotation schemes are currently used by parsers and corpora in biomedical information extraction. The recently introduced Stanford dependency scheme has been suggested to be a suitable unifying syntax formalism. In this paper, we present a step towards such unification by creating a conversion from the Link Grammar to the Stanford scheme. Further, we create a version of the BioInfer corpus with syntactic annotation in this scheme. We present an application-oriented evaluation of the transformation and assess the suitability of the scheme and our conversion to the unification of the syntactic annotations of BioInfer and the GENIA Treebank. We find that a highly reliable conversion is both feasible to create and practical, increasing the applicability of both the parser and the corpus to information extraction.