Putting hands to rest: efficient deep CNN-RNN architecture for chemical named entity recognition with no hand-crafted rules

Abstract

Chemical named entity recognition (NER) is an active field of research in biomedical natural language processing. To facilitate the development of new and superior chemical NER systems, BioCreative released the CHEMDNER corpus, an extensive dataset of diverse manually annotated chemical entities. Most of the systems trained on the corpus rely on complicated hand-crafted rules or curated databases for data preprocessing, feature extraction and output post-processing, though modern machine learning algorithms, such as deep neural networks, can automatically design the rules with little to none human intervention. Here we explored this approach by experimenting with various deep learning architectures for targeted tokenisation and named entity recognition. Our final model, based on a combination of convolutional and stateful recurrent neural networks with attention-like loops and hybrid word-and character-level embeddings, reaches near human-level performance on the testing dataset with no manually asserted rules. To make our model easily accessible for standalone use and integration in third-party software, we've developed a Python package with a minimalistic user interface.

A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records

“…(2) From the perspective of prognosis prediction, the use of machine learning methods for risk prediction has gradually become the trend of disease prediction, while machine learning methods such as random forest, decision tree, SVM, and other machine learning methods have achieved certain research results in the prediction of cerebrovascular diseases. Literature [13][14][15] constructed logistic regression, k-NN, random forest, decision tree, and SVM machine learning models based on follow-up data, and verified the advantages of machine learning models in cerebrovascular disease risk prediction. It shows that the effect of neural network learning score is better.…”