BANNER: an executable survey of advances in biomedical named entity recognition.

@article{citeulike:2775855, abstract = {There has been an increasing amount of research on biomedical named entity recognition, the most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text.}, address = {Department of Computer Science and Engineering, Arizona State University, USA.}, author = {Leaman, R. and Gonzalez, G. }, citeulike-article-id = {2775855}, issn = {1793-5091}, journal = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing}, keywords = {bionlp, crf, entity, gene, name, named, ner, recognition}, pages = {652--663}, posted-at = {2008-05-09 14:25:32}, priority = {2}, title = {BANNER: an executable survey of advances in biomedical named entity recognition.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/18229723}, year = {2008} }

TY - JOUR ID - citeulike:2775855 L3 - citeulike-article-id:2775855 TI - BANNER: an executable survey of advances in biomedical named entity recognition. JF - Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing SP - 652 EP - 663 AD - Department of Computer Science and Engineering, Arizona State University, USA. SN - 1793-5091 N2 - There has been an increasing amount of research on biomedical named entity recognition, the most basic text extraction problem, resulting in significant progress by different research teams around the world. This has created a need for a freely-available, open source system implementing the advances described in the literature. In this paper we present BANNER, an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field. BANNER is implemented in Java as a machine-learning system based on conditional random fields and includes a wide survey of the best techniques recently described in the literature. It is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps, and achieves significantly better performance than existing baseline systems. It is therefore useful to developers as an extensible NER implementation, to researchers as a standard for comparing innovative techniques, and to biologists requiring the ability to find novel entities in large amounts of text. KW - bionlp KW - crf KW - entity KW - gene KW - name KW - named KW - ner KW - recognition AU - Leaman, R AU - Gonzalez, G PY - 2008/// UR - http://view.ncbi.nlm.nih.gov/pubmed/18229723 ER -