[HTML][HTML] Character level and word level embedding with bidirectional LSTM–Dynamic recurrent neural network for biomedical named entity recognition from literature
Abstract Named Entity Recognition is the process of identifying different entities in a given
context. Biomedical Named Entity Recognition (BNER) is the task of extracting chemical …
context. Biomedical Named Entity Recognition (BNER) is the task of extracting chemical …
Machine learning and knowledge extraction in digital pathology needs an integrative approach
During the last decade pathology has benefited from the rapid progress of image digitizing
technologies, which led to the development of scanners, capable to produce so-called …
technologies, which led to the development of scanners, capable to produce so-called …
Deep-Confidentiality: An IoT-Enabled Privacy-Preserving Framework for Unstructured Big Biomedical Data
Due to the Internet of Things evolution, the clinical data is exponentially growing and using
smart technologies. The generated big biomedical data is confidential, as it contains a …
smart technologies. The generated big biomedical data is confidential, as it contains a …
A semi-supervised approach for extracting TCM clinical terms based on feature words
L Liu, X Wu, H Liu, X Cao, H Wang, H Zhou… - BMC Medical Informatics …, 2020 - Springer
Background A semi-supervised model is proposed for extracting clinical terms of Traditional
Chinese Medicine using feature words. Methods The extraction model is based on BiLSTM …
Chinese Medicine using feature words. Methods The extraction model is based on BiLSTM …
Named entity recognition for clinical portuguese corpus with conditional random fields and semantic groups
Considering the difficulties of extracting entities from Electronic Health Records (EHR) texts
in Portuguese, we explore the Conditional Random Fields (CRF) algorithm to build a Named …
in Portuguese, we explore the Conditional Random Fields (CRF) algorithm to build a Named …
A named entity recognition corpus for Vietnamese biomedical texts to support tuberculosis treatment
U Phan, PNV Nguyen, N Nguyen - Proceedings of the Thirteenth …, 2022 - aclanthology.org
Abstract Named Entity Recognition (NER) is an important task in information extraction.
However, due to the lack of labelled corpora, biomedical NER has scarcely been studied in …
However, due to the lack of labelled corpora, biomedical NER has scarcely been studied in …
[HTML][HTML] Exploring Biomedical Named Entity Recognition SciSpaCy and BioBERT Models
Methods Our paper explores the field of Biomedical Named Entity Recognition (BioNER) by
closely analysing two advanced models, SciSpaCy and BioBERT. We have made two …
closely analysing two advanced models, SciSpaCy and BioBERT. We have made two …
[PDF][PDF] Analysis of named-entity effect on text classification of traffic accident data using machine learning
AD Putra, AS Girsang - Indones. J. Electr. Eng. Comput. Sci, 2022 - academia.edu
With the rising number of accidents in Indonesia, it is still necessary to evaluate and analyze
accident data. The categorization of traffic accident data has been developed using word …
accident data. The categorization of traffic accident data has been developed using word …
Biomedical-named entity recognition using CUDA accelerated KNN algorithm
M Bali, AS Pichandi… - … Electronics and Control), 2023 - telkomnika.uad.ac.id
Biomedical named entity recognition (Bio-NER) is a highly complex and time-consuming
research domain using natural language processing (NLP). It's widely used in information …
research domain using natural language processing (NLP). It's widely used in information …
Named entity recognition for weather domain text in Hindi
G Yadav, SS Rathore… - International Journal of …, 2021 - inderscienceonline.com
Named entity recognition (NER) is the process of categorisation of a given entity in texts into
a corresponding pre-defined category such as PE for name of the person, LOC for the name …
a corresponding pre-defined category such as PE for name of the person, LOC for the name …