[HTML][HTML] AMMU: a survey of transformer-based biomedical pretrained language models
KS Kalyan, A Rajasekharan, S Sangeetha - Journal of biomedical …, 2022 - Elsevier
Transformer-based pretrained language models (PLMs) have started a new era in modern
natural language processing (NLP). These models combine the power of transformers …
natural language processing (NLP). These models combine the power of transformers …
Citesee: Augmenting citations in scientific papers with persistent and personalized historical context
When reading a scholarly article, inline citations help researchers contextualize the current
article and discover relevant prior work. However, it can be challenging to prioritize and …
article and discover relevant prior work. However, it can be challenging to prioritize and …
A survey on challenges and advances in natural language processing with a focus on legal informatics and low-resource languages
P Krasadakis, E Sakkopoulos, VS Verykios - Electronics, 2024 - mdpi.com
The field of Natural Language Processing (NLP) has experienced significant growth in
recent years, largely due to advancements in Deep Learning technology and especially …
recent years, largely due to advancements in Deep Learning technology and especially …
Ensemble transfer learning on augmented domain resources for oncological named entity recognition in Chinese clinical records
M Zhou, J Tan, S Yang, H Wang, L Wang, Z Xiao - IEEE Access, 2023 - ieeexplore.ieee.org
Biomedical Named Entity Recognition (NER) is a crucial task in Natural Language
Processing (NLP) and can help mine knowledge from massive clinical and diagnostic …
Processing (NLP) and can help mine knowledge from massive clinical and diagnostic …
[HTML][HTML] Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study
S Liu, A Wang, X Xiu, M Zhong, S Wu - JMIR Medical …, 2024 - medinform.jmir.org
Background: Named entity recognition (NER) models are essential for extracting structured
information from unstructured medical texts by identifying entities such as diseases …
information from unstructured medical texts by identifying entities such as diseases …
Few-shot named entity recognition: Definition, taxonomy and research directions
Recent years have seen an exponential growth (+ 98% in 2022 wrt the previous year) of the
number of research articles in the few-shot learning field, which aims at training machine …
number of research articles in the few-shot learning field, which aims at training machine …
S-NER: A concise and efficient span-based model for named entity recognition
J Yu, B Ji, S Li, J Ma, H Liu, H Xu - Sensors, 2022 - mdpi.com
Named entity recognition (NER) is a task that seeks to recognize entities in raw texts and is a
precondition for a series of downstream NLP tasks. Traditionally, prior NER models use the …
precondition for a series of downstream NLP tasks. Traditionally, prior NER models use the …
A multi-head adjacent attention-based pyramid layered model for nested named entity recognition
S Cui, I Joe - Neural Computing and Applications, 2023 - Springer
Named entity recognition (NER) is one of the widely studied natural language processing
tasks in recent years. Conventional solutions treat the NER as a sequence labeling problem …
tasks in recent years. Conventional solutions treat the NER as a sequence labeling problem …
Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition
For Named Entity Recognition (NER), sequence labeling-based and span-based paradigms
are quite different. Previous research has demonstrated that the two paradigms have clear …
are quite different. Previous research has demonstrated that the two paradigms have clear …
生物医学命名实体识别的两阶段学习算法
车翔玖， 徐欢， 潘明阳， 刘全乐 - 吉林大学学报(工学版), 2023 - xuebao.jlu.edu.cn
针对在生物医学领域中命名实体数据标注成本高, 难以获取大量有标签数据的问题,
提出了一个两阶段学习框架实现低资源下的中文生物医学命名实体识别. 在第一阶段 …
提出了一个两阶段学习框架实现低资源下的中文生物医学命名实体识别. 在第一阶段 …