BaseNPs that contain gene names: domain specificity and genericity
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
MMR-based active machine learning for bio named entity recognition
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Classifier subset selection for biomedical named entity recognition
Applied Intelligence
Clinical entity recognition using structural support vector machines with rich features
Proceedings of the ACM sixth international workshop on Data and text mining in biomedical informatics
Towards a Protein-Protein Interaction information extraction system: Recognizing named entities
Knowledge-Based Systems
| Hi-index | 3.84 |
Summary: POSBIOTM--NER is a trainable biomedical named-entity recognition system. POSBIOTM--NER can be automatically trained and adapted to new datasets without performance degradation, using CRF (conditional random field) machine learning techniques and automatic linguistic feature analysis. Currently, we have trained our system on three different datasets. GENIA--NER was trained based on GENIA Corpus, GENE--NER based on BioCreative data and GPCR--NER based on our own POSBIOTM/NE corpus, respectively, which would be used in GPCR-related pathway extraction. Availability: http://isoft.postech.ac.kr/Research/BioNER/POSBIOTM/NER/main.html Contact: songyu@postech.ac.kr