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

Journal of Cheminformatics

Ilia Korvigo¹  Mikhail Skoblov¹  Maxim Holmatov²  Anatolii Zaikovskii³ 

[1]Laboratory of Functional Analysis of the Genome, Moscow Institute of Physics and Technology

[2]St. Petersburg State Pediatric Medical University

[3]St. Petersburg State University

关键词: Named entities recognition;    Tokenisation;    Chemical;    Text mining;    Deep learning;    Recurrent neural network;   

DOI  :  10.1186/s13321-018-0280-0

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown