scholarly journals French Biomedical Text Simplification: When Small and Precise Helps

2020 ◽  
Author(s):  
Rémi Cardon ◽  
Natalia Grabar
Keyword(s):  
Biomedical Text ◽  
Text Simplification

GHS-NET A Generic Hybridized Shallow Neural Network for Multi-Label Biomedical Text Classification

2021 ◽  
pp. 103699
Author(s):  
Muhammad Ali Ibrahim ◽  
Muhammad Usman Ghani Khan ◽  
Faiza Mehmood ◽  
Muhammad Nabeel Asim ◽  
Waqar Mahmood
Keyword(s):  
Neural Network ◽  
Text Classification ◽  
Biomedical Text ◽  
Biomedical Text Classification

Automated Text Simplification

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Suha S. Al-Thanyyan ◽  
Aqil M. Azmi
Keyword(s):  
Reading Comprehension ◽  
Information Content ◽  
Evaluation Methods ◽  
Extensive Study ◽  
Low Literacy ◽  
Essential Tool ◽  
Text Simplification ◽  
Over Time ◽  
Original Information ◽  
Research Studies

Text simplification (TS) reduces the complexity of the text to improve its readability and understandability, while possibly retaining its original information content. Over time, TS has become an essential tool in helping those with low literacy levels, non-native learners, and those struggling with various types of reading comprehension problems. In addition, it is used in a preprocessing stage to enhance other NLP tasks. This survey presents an extensive study of current research studies in the field of TS, as well as covering resources, corpora, and evaluation methods that have been used in those studies.

scholarly journals Extraction of causal relations based on SBEL and BERT model

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Yifan Shao ◽  
Haoru Li ◽  
Jinghang Gu ◽  
Longhua Qian ◽  
Guodong Zhou
Keyword(s):  
State Of The Art ◽  
Causal Relation ◽  
Relation Extraction ◽  
The Other ◽  
Biomedical Text ◽  
Intermediate Form ◽  
Biomedical Text Mining ◽  
Causal Relations ◽  
The One ◽  
Stage 1

Abstract Extraction of causal relations between biomedical entities in the form of Biological Expression Language (BEL) poses a new challenge to the community of biomedical text mining due to the complexity of BEL statements. We propose a simplified form of BEL statements [Simplified Biological Expression Language (SBEL)] to facilitate BEL extraction and employ BERT (Bidirectional Encoder Representation from Transformers) to improve the performance of causal relation extraction (RE). On the one hand, BEL statement extraction is transformed into the extraction of an intermediate form—SBEL statement, which is then further decomposed into two subtasks: entity RE and entity function detection. On the other hand, we use a powerful pretrained BERT model to both extract entity relations and detect entity functions, aiming to improve the performance of two subtasks. Entity relations and functions are then combined into SBEL statements and finally merged into BEL statements. Experimental results on the BioCreative-V Track 4 corpus demonstrate that our method achieves the state-of-the-art performance in BEL statement extraction with F1 scores of 54.8% in Stage 2 evaluation and of 30.1% in Stage 1 evaluation, respectively. Database URL: https://github.com/grapeff/SBEL_datasets

Text Simplification with Self-Attention-Based Pointer-Generator Networks

2018 ◽  
pp. 537-545 ◽  
Author(s):  
Tianyu Li ◽  
Yun Li ◽  
Jipeng Qiang ◽  
Yun-Hao Yuan
Keyword(s):  
Text Simplification

Biomedical text summarisation using concept chains

2007 ◽  
Vol 1 (4) ◽  
pp. 389 ◽  
Author(s):  
Lawrence H. Reeve ◽  
Hyoil Han ◽  
Ari D. Brooks
Keyword(s):  
Biomedical Text

scholarly journals Automatic discourse connective detection in biomedical text

2012 ◽  
Vol 19 (5) ◽  
pp. 800-808 ◽  
Author(s):  
Balaji Polepalli Ramesh ◽  
Rashmi Prasad ◽  
Tim Miller ◽  
Brian Harrington ◽  
Hong Yu
Keyword(s):  
Biomedical Text
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