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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 Hits 1 – 20 of 61 1 GreaseLM: Graph REASoning Enhanced Language Models for Question Answering ... Zhang, Xikun; Bosselut, Antoine; Yasunaga, Michihiro. - : arXiv, 2022 BASE Show details 2 Human-like informative conversations: Better acknowledgements using conditional mutual information ... Paranjape, Ashwin; Manning, Christopher D.. - : arXiv, 2021 BASE Show details 3 ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts ... Koreeda, Yuta; Manning, Christopher D.. - : arXiv, 2021 BASE Show details 4 Biomedical and clinical English model packages for the Stanza Python NLP library Zhang, Yuhao; Zhang, Yuhui; Qi, Peng; Manning, Christopher D; Langlotz, Curtis P In: J Am Med Inform Assoc (2021) 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). Keyword: Research and Applications 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 BASE Hide details 5 Stanza: A Python Natural Language Processing Toolkit for Many Human Languages ... Qi, Peng; Zhang, Yuhao; Zhang, Yuhui. - : arXiv, 2020 BASE Show details 6 Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ... Nivre, Joakim; de Marneffe, Marie-Catherine; Ginter, Filip. - : arXiv, 2020 BASE Show details 7 Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation ... Dhole, Kaustubh D.; Manning, Christopher D.. - : arXiv, 2020 BASE Show details 8 Finding Universal Grammatical Relations in Multilingual BERT ... Chi, Ethan A.; Hewitt, John; Manning, Christopher D.. - : arXiv, 2020 BASE Show details 9 What Does BERT Look At? An Analysis of BERT's Attention ... Clark, Kevin; Khandelwal, Urvashi; Levy, Omer. - : arXiv, 2019 BASE Show details 10 CoQA: A Conversational Question Answering Challenge Reddy, Siva; Chen, Danqi; Manning, Christopher D. In: Transactions of the Association for Computational Linguistics, Vol 7, Pp 249-266 (2019) (2019) BASE Show details 11 Simpler but More Accurate Semantic Dependency Parsing ... Dozat, Timothy; Manning, Christopher D.. - : arXiv, 2018 BASE Show details 12 Arc-swift: A Novel Transition System for Dependency Parsing ... Qi, Peng; Manning, Christopher D.. - : arXiv, 2017 BASE Show details 13 CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies Çöltekin, Çağrı; Kayadelen, Tolga; Droganova, Kira. - : Association for Computational Linguistics, 2017. : country:USA, 2017. : place:Stroudsburg, PA, 2017 BASE Show details 14 Improving Coreference Resolution by Learning Entity-Level Distributed Representations ... Clark, Kevin; Manning, Christopher D.. - : arXiv, 2016 BASE Show details 15 A large annotated corpus for learning natural language inference ... Bowman, Samuel R.; Angeli, Gabor; Potts, Christopher. - : arXiv, 2015 BASE Show details 16 Text to 3D Scene Generation with Rich Lexical Grounding ... Chang, Angel; Monroe, Will; Savva, Manolis. - : arXiv, 2015 BASE Show details 17 Natural Language Processing: Integration of Automatic and Manual Analysis Eckart de Castilho, Richard [Verfasser]; Gurevych, Iryna [Akademischer Betreuer]; Henrich, Andreas [Akademischer Betreuer]. - Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2014 DNB Subject Category Language Show details 18 Learning Distributed Word Representations for Natural Logic Reasoning ... Bowman, Samuel R.; Potts, Christopher; Manning, Christopher D.. - : arXiv, 2014 BASE Show details 19 Discovery of Deep Structure from Unlabeled Data Ng, Andrew Y; Manning, Christopher D In: DTIC (2014) BASE Show details 20 Induced lexico-syntactic patterns improve information extraction from online medical forums Gupta, Sonal; MacLean, Diana L; Heer, Jeffrey. - : BMJ Publishing Group, 2014 BASE Show details Page: 1 2 3 4

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