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T.Tada

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文献紹介_202002_Is artificial data useful for biomedical Natural Language Processing algorithms?
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58
文献紹介_202001_A Novel System for Extractive Clinical Note Summarization using EHR Data
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150
文献紹介_201912_Publicly Available Clinical BERT Embeddings
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160
文献紹介_201911_EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
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210
文献紹介_201910_Do Neural NLP Models Know Numbers? Probing Numeracy in Embeddings
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100
文献紹介_201909_Sentence Mover’s Similarity_ Automatic Evaluation for Multi-Sentence Texts
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150
文献紹介_201908_Medical Word Embeddings for Spanish_ Development and Evaluation
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63
文献紹介_201907_Is Word Segmentation Necessary for Deep Learning of Chinese Representations
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94
文献紹介_201906_Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction
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94
文献紹介201905_Context-Aware Cross-Lingual Mapping
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94
文献紹介201904_Biomedical Document Retrieval for Clinical Decision Support System
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80
文献紹介201904_Exploring Semantic Properties of Sentence Embeddings
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140
文献紹介201902_Effectively Crowdsourcing Radiology Report Annotations
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99
文献紹介201901_Learning to Summarize Radiology Findings
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92
文献紹介20181211_On Learning Better Word Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data
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110
文献紹介20181126_Learning and Evaluating Interpretable Sentence Embeddings
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66
文献紹介_20181024_An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation
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130
文献紹介 : How to Train good Word Embeddings for Biomedical NLP
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140

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