A hybrid deep-learning approach for complex biochemical named entity recognition[Formula presented]

https://hdl.handle.net/10536/DRO/DU:30149453
Open external identifier URL

journal contribution

posted on 2024-06-06, 09:50 authored by J Liu, L Gao, S Guo, R Ding, X Huang, L Ye, Q Meng, Asef NazariAsef Nazari, Dhananjay ThiruvadyDhananjay Thiruvady

A hybrid deep-learning approach for complex biochemical named entity recognition[Formula presented]

History

Related Materials

1.
DOI - Is version of https://doi.org/10.1016/j.knosys.2021.106958

Language

English

Author URL

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000641333500022&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a045e4b2bb1f2b747c68c720ec8913b7

Publication classification

C1 Refereed article in a scholarly journal

Journal

Knowledge-Based Systems

Volume

221

Article number

ARTN 106958

ISSN

0950-7051

eISSN

1872-7409

Publisher

ELSEVIER

Usage metrics

Categories

No categories selected

Keywords

Science & Technology Technology Computer Science, Artificial Intelligence Computer Science Named entity recognition Deep learning Bi-directional Long Short-Term Memory (BILSTM)Conditional Random Field (CRF)Bidirectional Encoder Representations from Transformers (BERT)Multi-Head Attention (MHATT)LSTM-CRF ATTENTION MODEL NETWORKS 4612 Software engineering

Licence

All Rights Reserved

Exports