MedLinker: Medical Entity Linking with Neural Representations and Dictionary Matching
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[1] Daniel King,et al. ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing , 2019, BioNLP@ACL.
[2] Zhiyong Lu,et al. TaggerOne: joint named entity recognition and normalization with semi-Markov Models , 2016, Bioinform..
[3] Iz Beltagy,et al. SciBERT: A Pretrained Language Model for Scientific Text , 2019, EMNLP.
[4] Berry de Bruijn,et al. Recognizing UMLS Semantic Types with Deep Learning , 2019, EMNLP.
[5] Jaewoo Kang,et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining , 2019, Bioinform..
[6] Donghui Li,et al. MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts , 2019, AKBC.
[7] Tapio Salakoski,et al. Wide-scope biomedical named entity recognition and normalization with CRFs, fuzzy matching and character level modeling , 2018, Database J. Biol. Databases Curation.
[8] Naoaki Okazaki,et al. Simple and Efficient Algorithm for Approximate Dictionary Matching , 2010, COLING.
[9] Olivier Bodenreider,et al. The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..
[10] Luca Soldaini. QuickUMLS: a fast, unsupervised approach for medical concept extraction , 2016 .
[11] Zhiyong Lu,et al. Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets , 2019, BioNLP@ACL.
[12] Daniel Loureiro,et al. Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation , 2019, ACL.