Y
Yuhui Zhang
Researcher at Tsinghua University
Publications - 17
Citations - 1568
Yuhui Zhang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Tokenization (data security). The author has an hindex of 7, co-authored 11 publications receiving 583 citations. Previous affiliations of Yuhui Zhang include Stanford University.
Papers
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Journal ArticleDOI
Biomedical and clinical English model packages for the Stanza Python NLP library
TL;DR: This paper used the Stanza NLP library for syntactic analysis and named entity recognition of biomedical and clinical English text, and achieved state-of-the-art performance on the CRAFT shared task.
Proceedings ArticleDOI
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System
Guo Zhipeng,Xiaoyuan Yi,Maosong Sun,Wenhao Li,Cheng Yang,Jiannan Liang,Huimin Chen,Yuhui Zhang,Ruoyu Li +8 more
TL;DR: Jiuge is proposed, a human-machine collaborative Chinese classical poetry generation system that allows users to revise the unsatisfied parts of a generated poem draft repeatedly and allows constant and active participation of users in poetic creation.
Posted Content
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
TL;DR: Stanza as mentioned in this paper is an open-source Python NLP toolkit supporting 66 human languages, including English, French, German, Dutch, Russian, Japanese, and Chinese. But it does not have a language-agnostic fully neural pipeline.
Journal ArticleDOI
DeepTag: inferring diagnoses from veterinary clinical notes.
Allen Nie,Ashley M. Zehnder,Rodney L. Page,Yuhui Zhang,Arturo Lopez Pineda,Manuel A. Rivas,Carlos Bustamante,James Zou +7 more
TL;DR: A deep learning algorithm, DeepTag, which automatically infers diagnostic codes from veterinary free-text notes and enables automated disease annotation across a broad range of clinical diagnoses with minimal preprocessing.
Journal ArticleDOI
VetTag: improving automated veterinary diagnosis coding via large-scale language modeling.
TL;DR: A large-scale algorithm to automatically predict all 4577 standard veterinary diagnosis codes from free text and shows that hierarchical training can address severe data imbalances for fine-grained diagnosis with a few training cases, and adds insights into the power of unsupervised learning for clinical natural language processing.