A survey on deep learning for named entity recognition

J Li, A Sun, J Han, C Li - IEEE transactions on knowledge and …, 2020‏ - ieeexplore.ieee.org
Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Domain adaptation: challenges, methods, datasets, and applications

P Singhal, R Walambe, S Ramanna, K Kotecha - IEEE access, 2023‏ - ieeexplore.ieee.org
Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

An introduction to deep learning in natural language processing: Models, techniques, and tools

I Lauriola, A Lavelli, F Aiolli - Neurocomputing, 2022‏ - Elsevier
Abstract Natural Language Processing (NLP) is a branch of artificial intelligence that
involves the design and implementation of systems and algorithms able to interact through …

Domain-specific language model pretraining for biomedical natural language processing

Y Gu, R Tinn, H Cheng, M Lucas, N Usuyama… - ACM Transactions on …, 2021‏ - dl.acm.org
Pretraining large neural language models, such as BERT, has led to impressive gains on
many natural language processing (NLP) tasks. However, most pretraining efforts focus on …

An extensive benchmark study on biomedical text generation and mining with ChatGPT

Q Chen, H Sun, H Liu, Y Jiang, T Ran, X **… - …, 2023‏ - academic.oup.com
Motivation In recent years, the development of natural language process (NLP) technologies
and deep learning hardware has led to significant improvement in large language models …

ScispaCy: fast and robust models for biomedical natural language processing

M Neumann, D King, I Beltagy, W Ammar - arxiv preprint arxiv …, 2019‏ - arxiv.org
Despite recent advances in natural language processing, many statistical models for
processing text perform extremely poorly under domain shift. Processing biomedical and …

A critical assessment of using ChatGPT for extracting structured data from clinical notes

J Huang, DM Yang, R Rong, K Nezafati, C Treager… - npj Digital …, 2024‏ - nature.com
Existing natural language processing (NLP) methods to convert free-text clinical notes into
structured data often require problem-specific annotations and model training. This study …

Advancing entity recognition in biomedicine via instruction tuning of large language models

VK Keloth, Y Hu, Q **e, X Peng, Y Wang… - …, 2024‏ - academic.oup.com
Abstract Motivation Large Language Models (LLMs) have the potential to revolutionize the
field of Natural Language Processing, excelling not only in text generation and reasoning …

Empirical study of zero-shot ner with chatgpt

T **e, Q Li, J Zhang, Y Zhang, Z Liu, H Wang - arxiv preprint arxiv …, 2023‏ - arxiv.org
Large language models (LLMs) exhibited powerful capability in various natural language
processing tasks. This work focuses on exploring LLM performance on zero-shot information …

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

H Wu, M Wang, J Wu, F Francis, YH Chang… - NPJ digital …, 2022‏ - nature.com
Much of the knowledge and information needed for enabling high-quality clinical research is
stored in free-text format. Natural language processing (NLP) has been used to extract …