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BANNER: An Executable Survey of Advances in Biomedical Named Entity Recognition
BANNER is an open-source, executable survey of advances in biomedical named entity recognition, intended to serve as a benchmark for the field and is designed to maximize domain independence by not employing brittle semantic features or rule-based processing steps.
BioCreative V CDR task corpus: a resource for chemical disease relation extraction
The BC5CDR corpus was successfully used for the BioCreative V challenge tasks and should serve as a valuable resource for the text-mining research community.
NCBI disease corpus: A resource for disease name recognition and concept normalization
DNorm: disease name normalization with pairwise learning to rank
This article introduces the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM, a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data.
Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task
- Chih-Hsuan Wei, Yifan Peng, Zhiyong Lu
- Computer ScienceDatabase J. Biol. Databases Curation
- 19 March 2016
This task was found to be successful in engaging the text-mining research community, producing a large annotated corpus and improving the results of automatic disease recognition and CDR extraction.
TaggerOne: joint named entity recognition and normalization with semi-Markov Models
This work proposes the first machine learning model for joint NER and normalization during both training and prediction, which is trainable for arbitrary entity types and consists of a semi-Markov structured linear classifier, with a rich feature approach for N ER and supervised semantic indexing for normalization.
Inter-species normalization of gene mentions with GNAT
- J. Hakenberg, C. Plake, Robert Leaman, M. Schroeder, Graciela Gonzalez
- Computer Science, BiologyECCB
- 15 August 2008
The first publicly available system, GNAT, reported to handle inter-species GN, uses extensive background knowledge on genes to resolve ambiguous names to EntrezGene identifiers and performs comparably to single-species approaches proposed by us and others.
Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks
- Robert Leaman, L. Wojtulewicz, R. Sullivan, A. Skariah, Jian Yang, Graciela Gonzalez
- 15 July 2010
It is concluded that user comments pose a significant natural language processing challenge, but do contain useful extractable information which merits further exploration and is evaluated on a manually annotated set of user comments with promising performance.
Overview of BioCreative II gene normalization
Major advances for the BioCreative II gene normalization task include broader participation (20 versus 8 teams) and a pooled system performance comparable to human experts, at over 90% agreement, which show promise as tools to link the literature with biological databases.
PubTator central: automated concept annotation for biomedical full text articles
- Chih-Hsuan Wei, Alexis Allot, Robert Leaman, Zhiyong Lu
- Computer ScienceNucleic Acids Res.
- 22 May 2019
The full text results in PTC significantly increase biomedical concept coverage and it is anticipated this expansion will both enhance existing downstream applications and enable new use cases.