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Proficiency and the Use of Machine Translation: A Case Study of Four Japanese Learners
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In: L2 Journal, vol 14, iss 1 (2022)
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| 2 |
Discovering Dialog Structure Graph for Coherent Dialog Generation ...
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| 3 |
Functional Investigation of a GRIN2A Variant Associated with Rolandic Epilepsy
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Additional file 1: of High HIV incidence epidemic among men who have sex with men in china: results from a multi-site cross-sectional study ...
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Additional file 1: of High HIV incidence epidemic among men who have sex with men in china: results from a multi-site cross-sectional study ...
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| 7 |
Patient Mutations of the Intellectual Disability Gene KDM5C Down-Regulate Netrin G2 and Suppress Neurite Growth in Neuro2a Cells
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| 8 |
A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text
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Abstract:
Clinical Named Entity Recognition (NER) is a critical task for extracting important patient information from clinical text to support clinical and translational research. This study explored the neural word embeddings derived from a large unlabeled clinical corpus for clinical NER. We systematically compared two neural word embedding algorithms and three different strategies for deriving distributed word representations. Two neural word embeddings were derived from the unlabeled Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II corpus (403,871 notes). The results from both 2010 i2b2 and 2014 Semantic Evaluation (SemEval) data showed that the binarized word embedding features outperformed other strategies for deriving distributed word representations. The binarized embedding features improved the F1-score of the Conditional Random Fields based clinical NER system by 2.3% on i2b2 data and 2.4% on SemEval data. The combined feature from the binarized embeddings and the Brown clusters improved the F1-score of the clinical NER system by 2.9% on i2b2 data and 2.7% on SemEval data. Our study also showed that the distributed word embedding features derived from a large unlabeled corpus can be better than the widely used Brown clusters. Further analysis found that the neural word embeddings captured a wide range of semantic relations, which could be discretized into distributed word representations to benefit the clinical NER system. The low-cost distributed feature representation can be adapted to any other clinical natural language processing research.
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Keyword:
Articles
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765694/
http://www.ncbi.nlm.nih.gov/pubmed/26958273
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| 15 |
进入傅雷译作 ; : Donner accès à l'oeuvre de Fu Lei
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In: 傅雷的精神世界及其时代意义 (Le monde spirituel de Fu Lei et sa signification dans le temps) ; https://halshs.archives-ouvertes.fr/halshs-00685093 ; 许均 (XU Jun). 傅雷的精神世界及其时代意义 (Le monde spirituel de Fu Lei et sa signification dans le temps), 中西书局, 上海 (Publication de l'Ouest, Shanghai), pp.351-366, 2011 (2011)
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| 17 |
Displaying overt recipiency: reactive tokens in Mandarin task-oriented conversation
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| 18 |
The prosody of interrogatives at transition-relevance places in Mandarin Chinese conversation
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