Extraction and search of chemical formulae in text documents on the web
Proceedings of the 16th international conference on World Wide Web
Kernel-based learning for biomedical relation extraction
Journal of the American Society for Information Science and Technology
Mining, indexing, and searching for textual chemical molecule information on the web
Proceedings of the 17th international conference on World Wide Web
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
BioNoculars: extracting protein-protein interactions from biomedical text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
SETQA-NLP '09 Proceedings of the Workshop on Software Engineering, Testing, and Quality Assurance for Natural Language Processing
Assigning roles to protein mentions: The case of transcription factors
Journal of Biomedical Informatics
Classifying relations for biomedical named entity disambiguation
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
An Overview of BioCreative II.5
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
An IR-Aided Machine Learning Framework for the BioCreative II.5 Challenge
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Improving verbose queries using subset distribution
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploiting sequential relationships for familial classification
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Automatic identification of biomedical concepts in spanish-language unstructured clinical texts
Proceedings of the 1st ACM International Health Informatics Symposium
Journal of Biomedical Informatics
Detecting hedge cues and their scope in biomedical text with conditional random fields
Journal of Biomedical Informatics
Expansion finding for given acronyms using conditional random fields
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Mining methodologies from NLP publications: A case study in automatic terminology recognition
Computer Speech and Language
Adapting a general semantic interpretation approach to biological event extraction
BioNLP Shared Task '11 Proceedings of the BioNLP Shared Task 2011 Workshop
Incremental maintenance of biological databases using association rule mining
PRIB'06 Proceedings of the 2006 international conference on Pattern Recognition in Bioinformatics
Mining protein-protein interactions from GeneRIFs with OpenDMAP
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Extracting and normalizing gene/protein mentions with the flexible and trainable moara java library
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Adding text mining workflows as web services to the BioCatalogue
Proceedings of the 4th International Workshop on Semantic Web Applications and Tools for the Life Sciences
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Variational conditional random fields for online speaker detection and tracking
Speech Communication
Using an ensemble system to improve concept extraction from clinical records
Journal of Biomedical Informatics
Effective named entity recognition for idiosyncratic web collections
Proceedings of the 23rd international conference on World wide web
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Summary: ABNER (A Biomedical Named Entity Recognizer) is an open source software tool for molecular biology text mining. At its core is a machine learning system using conditional random fields with a variety of orthographic and contextual features. The latest version is 1.5, which has an intuitive graphical interface and includes two modules for tagging entities (e.g. protein and cell line) trained on standard corpora, for which performance is roughly state of the art. It also includes a Java application programming interface allowing users to incorporate ABNER into their own systems and train models on new corpora. Availability: ABNER is available as an executable Java archive and source code from http://www.cs.wisc.edu/~bsettles/abner/ Contact: bsettles@cs.wisc.edu