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GreaseLM: Graph REASoning Enhanced Language Models for Question Answering ...
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Human-like informative conversations: Better acknowledgements using conditional mutual information ...
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ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts ...
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Biomedical and clinical English model packages for the Stanza Python NLP library
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In: J Am Med Inform Assoc (2021)
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Abstract:
OBJECTIVE: The study sought to develop and evaluate neural natural language processing (NLP) packages for the syntactic analysis and named entity recognition of biomedical and clinical English text. MATERIALS AND METHODS: We implement and train biomedical and clinical English NLP pipelines by extending the widely used Stanza library originally designed for general NLP tasks. Our models are trained with a mix of public datasets such as the CRAFT treebank as well as with a private corpus of radiology reports annotated with 5 radiology-domain entities. The resulting pipelines are fully based on neural networks, and are able to perform tokenization, part-of-speech tagging, lemmatization, dependency parsing, and named entity recognition for both biomedical and clinical text. We compare our systems against popular open-source NLP libraries such as CoreNLP and scispaCy, state-of-the-art models such as the BioBERT models, and winning systems from the BioNLP CRAFT shared task. RESULTS: For syntactic analysis, our systems achieve much better performance compared with the released scispaCy models and CoreNLP models retrained on the same treebanks, and are on par with the winning system from the CRAFT shared task. For NER, our systems substantially outperform scispaCy, and are better or on par with the state-of-the-art performance from BioBERT, while being much more computationally efficient. CONCLUSIONS: We introduce biomedical and clinical NLP packages built for the Stanza library. These packages offer performance that is similar to the state of the art, and are also optimized for ease of use. To facilitate research, we make all our models publicly available. We also provide an online demonstration (http://stanza.run/bio).
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Keyword:
Research and Applications
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8363782/ https://doi.org/10.1093/jamia/ocab090 http://www.ncbi.nlm.nih.gov/pubmed/34157094
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Stanza: A Python Natural Language Processing Toolkit for Many Human Languages ...
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Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ...
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Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation ...
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Finding Universal Grammatical Relations in Multilingual BERT ...
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CoQA: A Conversational Question Answering Challenge
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In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 249-266 (2019) (2019)
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Arc-swift: A Novel Transition System for Dependency Parsing ...
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CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
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Improving Coreference Resolution by Learning Entity-Level Distributed Representations ...
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A large annotated corpus for learning natural language inference ...
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Text to 3D Scene Generation with Rich Lexical Grounding ...
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Learning Distributed Word Representations for Natural Logic Reasoning ...
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Discovery of Deep Structure from Unlabeled Data
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In: DTIC (2014)
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Induced lexico-syntactic patterns improve information extraction from online medical forums
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