Biomedical and clinical English model packages for the Stanza Python NLP library.

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 com

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8363782 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature