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Background: Named Entity Recognition (NER) and Normalisation (NEN) are core components of any text-mining system for biomedical texts.
Performance measures for information extraction
John Makhoul, Francis Kubala, Richard Schwartz, and Ralph Weischedel · 1999
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A new method to measure the semantic similarity of GO terms
James Z. Wang, Zhidian Du, Rapeeporn Payattakool, Philip S. Yu, and Chin-Fu Chen · 2007
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Abbreviation definition identification based on automatic precision estimates
Sunghwan Sohn, Donald C. Comeau, Won Kim, and W. John Wilbur · 2008
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Concept annotation in the CRAFT corpus
Michael Bada, Miriam Eckert, Donald Evans, Kristin Garcia, Krista Shipley, Dmitry Sitnikov, William A. Baumgartner, K. Bretonnel Cohen, Karin Verspoor, Judith A. Blake, and Lawrence E. Hunter · 2012
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A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools
Karin Verspoor, Kevin Bretonnel Cohen, Arrick Lanfranchi, Colin Warner, Helen L. Johnson, Christophe Roeder, Jinho D. Choi, Christopher Funk, Yuriy Malenkiy, Miriam Eckert, Nianwen Xue, William A. Baumgartner, Michael Bada, Martha Palmer, and Lawrence E. Hunter · 2012
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Gimli: open source and high-performance biomedical name recognition
David Campos, Sérgio Matos, and José Luís Oliveira · 2013
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A modular framework for biomedical concept recognition
David Campos, Sérgio Matos, and José Luís Oliveira · 2013
Earlier work this paper cites.
DNorm: disease name normalization with pairwise learning to rank
Robert Leaman, Rezarta Islamaj Doğan, and Zhiyong Lu · 2013
Earlier work this paper cites.
BioNLP shared task 2013 – an overview of the bacteria biotope task
Robert Bossy, Wiktoria Golik, Zorana Ratkovic, Philippe Bessières, and Claire Nédellec · 2013
Earlier work this paper cites.
Sieve-based entity linking for the biomedical domain
Jennifer D’Souza and Vincent Ng · 2015
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Assessing the impact of case sensitivity and term information gain on biomedical concept recognition
Tudor Groza and Karin Verspoor · 2015
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DTMiner: identification of potential disease targets through biomedical literature mining
Dong Xu, Meizhuo Zhang, Yanping Xie, Fan Wang, Ming Chen, Kenny Q. Zhu, and Jia Wei · 2016
Earlier work this paper cites.
NOBLE – Flexible concept recognition for large-scale biomedical natural language processing
Eugene Tseytlin, Kevin Mitchell, Elizabeth Legowski, Julia Corrigan, Girish Chavan, and Rebecca S. Jacobson · 2016
Earlier work this paper cites.
TaggerOne: joint named entity recognition and normalization with semi-Markov Models
Robert Leaman and Zhiyong Lu · 2016
Earlier work this paper cites.
RysannMD: a biomedical semantic annotator balancing speed and accuracy
John Cuzzola, Jelena Jovanović, and Ebrahim Bagheri · 2017
Cited alongside, same era.
Joint Entity Recognition and Linking in Technical Domains Using Undirected Probabilistic Graphical Models
Hendrik ter Horst, Matthias Hartung, and Philipp Cimiano · 2017
Cited alongside, same era.
A transition-based joint model for disease named entity recognition and normalization
Yinxia Lou, Yue Zhang, Tao Qian, Fei Li, Shufeng Xiong, and Donghong Ji · 2017
Cited alongside, same era.
The Colorado Richly Annotated Full Text (CRAFT) Corpus: Multi-Model Annotation in the Biomedical Domain
K. Bretonnel Cohen, Karin Verspoor, Karën Fort, Christopher Funk, Michael Bada, Martha Palmer, and Lawrence E. Hunter · 2017
Cited alongside, same era.
A neural network multi-task learning approach to biomedical named entity recognition
Gamal Crichton, Sampo Pyysalo, Billy Chiu, and Anna Korhonen · 2017
Cited alongside, same era.
Biomedical concept recognition using deep neural sequence models
Negacy D. Hailu, Michael Bada, Asmelash Teka Hadgu, and Lawrence E. Hunter · 2019
Later among the works it cites.
Design, implementation, and operation of a rapid, robust named entity recognition web service
Sune Pletscher-Frankild and Lars Juhl Jensen · 2019
Later among the works it cites.
OGER++: hybrid multi-type entity recognition
Lenz Furrer, Anna Jancso, Nicola Colic, and Fabio Rinaldi · 2019
Later among the works it cites.
A neural multi-task learning framework to jointly model medical named entity recognition and normalization
Sendong Zhao, Ting Liu, Sicheng Zhao, and Fei Wang · 2019
Later among the works it cites.
CRAFT shared tasks 2019 overview – integrated structure, semantics, and coreference
William Baumgartner, Michael Bada, Sampo Pyysalo, Manuel R. Ciosici, Negacy Hailu, Harrison Pielke-Lombardo, Michael Regan, and Lawrence Hunter · 2019
Later among the works it cites.
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Entity recognition in the biomedical domain using a hybrid approach
Marco Basaldella, Lenz Furrer, Carlo Tasso, and Fabio Rinaldi · 2017
Cited alongside, same era.
OGER: OntoGene’s entity recogniser in the BeCalm TIPS task
Lenz Furrer and Fabio Rinaldi · 2017
Cited alongside, same era.
Gene ontology concept recognition using named concept: understanding the various presentations of the gene functions in biomedical literature
Chia-Jung Yang and Jung-Hsien Chiang · 2018
Cited alongside, same era.
Improving precision in concept normalization
Mayla Boguslav, K. Bretonnel Cohen, William A. Baumgartner, Jr., and Lawrence E. Hunter · 2018
Cited alongside, same era.
HUNER: improving biomedical NER with pretraining
Leon Weber, Jannes Münchmeyer, Tim Rocktäschel, Maryam Habibi, and Ulf Leser · 2019
Cited alongside, same era.
Towards reliable named entity recognition in the biomedical domain
John M Giorgi and Gary D Bader · 2019
Cited alongside, same era.
SciBERT: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
Cited alongside, same era.
Investigation of traditional and deep neural sequence models for biomedical concept recognition
Negacy Degefa Hailu · 2019
Later among the works it cites.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang · 2019
Later among the works it cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Later among the works it cites.
UZH@CRAFT-ST: a sequence-labeling approach to concept recognition
Lenz Furrer, Joseph Cornelius, and Fabio Rinaldi · 2019
Later among the works it cites.
DTranNER: biomedical named entity recognition with deep learning-based label-label transition model
S. K. Hong and Jae-Gil Lee · 2020
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https://github.com/spyysalo/standoff2conll
standoff2conll. Conversion from brat-flavored standoff to CoNLL format · 2020
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https://github.com/lfurrer/standoff2conll
standoff2conll. Forked from spyysalo/standoff2conll · 2020
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https://github.com/UCDenver-ccp/craft-shared-tasks
CRAFT shared task evaluation. Code and scripts used for evaluation of the CRAFT Shared Tasks 2019 · 2020
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