Referent graph embedding model for name entity recognition of Chinese car reviews

2021 ◽  
pp. 107558
Author(s):  
Zhao Fang ◽  
Qiang Zhang ◽  
Stanley Kok ◽  
Ling Li ◽  
Anning Wang ◽  
...  
Keyword(s):  
Graph Embedding ◽  
Name Entity Recognition ◽  
Entity Recognition ◽  
Name Entity

Developing Name Entity Recognition for Structured and Unstructured Text Formatting Dataset

2020 ◽  
Author(s):  
Nadhia Salsabila Azzahra ◽  
Muhammad Okky Ibrohim ◽  
Junaedi Fahmi ◽  
Bagus Fajar Apriyanto ◽  
Oskar Riandi
Keyword(s):  
Name Entity Recognition ◽  
Entity Recognition ◽  
Unstructured Text ◽  
Name Entity

Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs

2019 ◽  
Vol 76 (8) ◽  
pp. 6399-6420 ◽  
Author(s):  
Qing Zhao ◽  
Dan Wang ◽  
Jianqiang Li ◽  
Faheem Akhtar
Keyword(s):  
Name Entity Recognition ◽  
Entity Recognition ◽  
Name Entity

scholarly journals ENCADEAr: ENCADEAmento automático de notícias

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Carla Abreu ◽  
Jorge Teixeira ◽  
Eugénio Oliveira
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Supervised Learning ◽  
Language Processing ◽  
Name Entity Recognition ◽  
Entity Recognition ◽  
Name Entity ◽  
Supervised Learning Algorithms ◽  
Processing Information

This work aims at defining and evaluating different techniques to automatically build temporal news sequences. The approach proposed is composed by three steps: (i) near duplicate documents detention; (ii) keywords extraction; (iii) news sequences creation. This approach is based on: Natural Language Processing, Information Extraction, Name Entity Recognition and supervised learning algorithms. The proposed methodology got a precision of 93.1% for news chains sequences creation.

scholarly journals Accurate Name Entity Recognition for Biomedical Literatures: A Combined High-quality Manual Annotation and Deep-learning Natural Language Processing Study

2021 ◽  
Author(s):  
Dao-Ling Huang ◽  
Quanlei Zeng ◽  
Yun Xiong ◽  
Shuixia Liu ◽  
Chaoqun Pang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Name Entity Recognition ◽  
Entity Recognition ◽  
Manual Annotation ◽  
Gene Variant ◽  
High Quality ◽  
Name Entity ◽  
Entity Annotation

A combined high-quality manual annotation and deep-learning natural language processing study is reported to make accurate name entity recognition (NER) for biomedical literatures. A home-made version of entity annotation guidelines on biomedical literatures was constructed. Our manual annotations have an overall over 92% consistency for all the four entity types such as gene, variant, disease and species with the same publicly available annotated corpora from other experts previously. A total of 400 full biomedical articles from PubMed are annotated based on our home-made entity annotation guidelines. Both a BERT-based large model and a DistilBERT-based simplified model were constructed, trained and optimized for offline and online inference, respectively. The F1-scores of NER of gene, variant, disease and species for the BERT-based model are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those for the DistilBERT-based model are 95.14%, 86.26%, 91.37% and 89.92%, respectively. The F1 scores of the DistilBERT-based NER model retains 97.8%, 92.2%, 98.7% and 93.9% of those of BERT-based NER for gene, variant, disease and species, respectively. Moreover, the performance for both our BERT-based NER model and DistilBERT-based NER model outperforms that of the state-of-art model,BioBERT, indicating the significance to train an NER model on biomedical-domain literatures jointly with high-quality annotated datasets.

scholarly journals Evaluating and Combining Name Entity Recognition Systems

2016 ◽  
Author(s):  
Ridong Jiang ◽  
Rafael E. Banchs ◽  
Haizhou Li
Keyword(s):  
Name Entity Recognition ◽  
Entity Recognition ◽  
Name Entity ◽  
Recognition Systems
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