Hedge Trimmer: a parse-and-trim approach to headline generation
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
A composite kernel to extract relations between entities with both flat and structured features
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the 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
RelEx---Relation extraction using dependency parse trees
Bioinformatics
Beyond SumBasic: Task-focused summarization with sentence simplification and lexical expansion
Information Processing and Management: an International Journal
Foundations and Trends in Databases
A graph kernel for protein-protein interaction extraction
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Static relations: a piece in the biomedical information extraction puzzle
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Learning the scope of hedge cues in biomedical texts
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Efficient HPSG parsing with supertagging and CFG-filtering
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Comparative experiments on learning information extractors for proteins and their interactions
Artificial Intelligence in Medicine
Simple English Wikipedia: a new text simplification task
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Neighborhood hash graph kernel for protein-protein interaction extraction
Journal of Biomedical Informatics
Learning to simplify sentences using Wikipedia
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
Learning bayesian network using parse trees for extraction of protein-protein interaction
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
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Relations between entities in text have been widely researched in the natural language processing and information-extraction communities. The region connecting a pair of entities (in a parsed sentence) is often used to construct kernels or feature vectors that can recognize and extract interesting relations. Such regions are useful, but they can also incorporate unnecessary distracting information. In this paper, we propose a rule-based method to remove the information that is unnecessary for relation extraction. Protein-protein interaction (PPI) is used as an example relation extraction problem. A dozen simple rules are defined on output from a deep parser. Each rule specifically examines the entities in one target interaction pair. These simple rules were tested using several PPI corpora. The PPI extraction performance was improved on all the PPI corpora.