An Unsupervised Approach for Cause-Effect Relation Extraction from Biomedical Text

2018 ◽  
pp. 419-427
Author(s):  
Raksha Sharma ◽  
Girish Palshikar ◽  
Sachin Pawar
Keyword(s):  
Relation Extraction ◽  
Biomedical Text ◽  
Unsupervised Approach ◽  
Effect Relation

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

scholarly journals Enhancing Biomedical Text Summarization Using Semantic Relation Extraction

PLoS ONE ◽  
2011 ◽  
Vol 6 (8) ◽  
pp. e23862 ◽  
Author(s):  
Yue Shang ◽  
Yanpeng Li ◽  
Hongfei Lin ◽  
Zhihao Yang
Keyword(s):  
Relation Extraction ◽  
Semantic Relation ◽  
Text Summarization ◽  
Biomedical Text

scholarly journals Coreference based event-argument relation extraction on biomedical text

2011 ◽  
Vol 2 (Suppl 5) ◽  
pp. S6 ◽  
Author(s):  
Katsumasa Yoshikawa ◽  
Sebastian Riedel ◽  
Tsutomu Hirao ◽  
Masayuki Asahara ◽  
Yuji Matsumoto
Keyword(s):  
Relation Extraction ◽  
Biomedical Text ◽  
Event Argument

scholarly journals Identifying interactions between chemical entities in biomedical text

2014 ◽  
Vol 11 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Andre Lamurias ◽  
João D. Ferreira ◽  
Francisco M. Couto
Keyword(s):  
Named Entity Recognition ◽  
Relation Extraction ◽  
Ensemble Classifier ◽  
Entity Recognition ◽  
Support Vector ◽  
Biomedical Text ◽  
Web Tool ◽  
Named Entity ◽  
Vector Machines ◽  
Chemical Named Entity Recognition

Summary Interactions between chemical compounds described in biomedical text can be of great importance to drug discovery and design, as well as pharmacovigilance. We developed a novel system, “Identifying Interactions between Chemical Entities” (IICE), to identify chemical interactions described in text. Kernel-based Support Vector Machines first identify the interactions and then an ensemble classifier validates and classifies the type of each interaction. This relation extraction module was evaluated with the corpus released for the DDI Extraction task of SemEval 2013, obtaining results comparable to stateof- the-art methods for this type of task. We integrated this module with our chemical named entity recognition module and made the whole system available as a web tool at www.lasige.di.fc.ul.pt/webtools/iice.

scholarly journals A neural joint model for entity and relation extraction from biomedical text

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Fei Li ◽  
Meishan Zhang ◽  
Guohong Fu ◽  
Donghong Ji
Keyword(s):  
Relation Extraction ◽  
Joint Model ◽  
Biomedical Text

scholarly journals Simultaneously Linking Entities and Extracting Relations from Biomedical Text without Mention-Level Supervision

2020 ◽  
Vol 34 (05) ◽  
pp. 7407-7414
Author(s):  
Trapit Bansal ◽  
Pat Verga ◽  
Neha Choudhary ◽  
Andrew McCallum
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Training Data ◽  
Biomedical Text ◽  
Entity Linking ◽  
Distant Supervision ◽  
Entity Relationships ◽  
And Training

Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision – which is not available in many domains. Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. This approach avoids cascading errors that arise from pipelined methods and more accurately predicts entity relationships from text. We show that our model outperforms a state-of-the art entity linking and relation extraction pipeline on two biomedical datasets and can drastically improve the overall recall of the system.

Automatic Cause-Effect Relation Extraction from Dental Textbooks Using BERT

2021 ◽  
pp. 127-138
Author(s):  
Terapat Chansai ◽  
Ruksit Rojpaisarnkit ◽  
Teerakarn Boriboonsub ◽  
Suppawong Tuarob ◽  
Myat Su Yin ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Effect Relation
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