A survey on extraction of causal relations from natural language text
As an essential component of human cognition, cause–effect relations appear frequently in
text, and curating cause–effect relations from text helps in building causal networks for …
text, and curating cause–effect relations from text helps in building causal networks for …
[HTML][HTML] Causal relationship extraction from biomedical text using deep neural models: A comprehensive survey
A Akkasi, MF Moens - Journal of biomedical informatics, 2021 - Elsevier
The identification of causal relationships between events or entities within biomedical texts
is of great importance for creating scientific knowledge bases and is also a fundamental …
is of great importance for creating scientific knowledge bases and is also a fundamental …
Knowledge-oriented convolutional neural network for causal relation extraction from natural language texts
Causal relation extraction is a challenging yet very important task for Natural Language
Processing (NLP). There are many existing approaches developed to tackle this task, either …
Processing (NLP). There are many existing approaches developed to tackle this task, either …
Causality extraction based on self-attentive BiLSTM-CRF with transferred embeddings
Causality extraction from natural language texts is a challenging open problem in artificial
intelligence. Existing methods utilize patterns, constraints, and machine learning techniques …
intelligence. Existing methods utilize patterns, constraints, and machine learning techniques …
Automatic extraction of causal relations from text using linguistically informed deep neural networks
In this paper we have proposed a linguistically informed recursive neural network
architecture for automatic extraction of cause-effect relations from text. These relations can …
architecture for automatic extraction of cause-effect relations from text. These relations can …
[PDF][PDF] Extracting causal knowledge from a medical database using graphical patterns
CSG Khoo, S Chan, Y Niu - … of the 38th annual meeting of the …, 2000 - aclanthology.org
This paper reports the first part of a project that aims to develop a knowledge extraction and
knowledge discovery system that extracts causal knowledge from textual databases. In this …
knowledge discovery system that extracts causal knowledge from textual databases. In this …
Financial document causality detection shared task (fincausal 2020)
D Mariko, HA Akl, E Labidurie, S Durfort… - arXiv preprint arXiv …, 2020 - arxiv.org
We present the FinCausal 2020 Shared Task on Causality Detection in Financial
Documents and the associated FinCausal dataset, and discuss the participating systems …
Documents and the associated FinCausal dataset, and discuss the participating systems …
Financial causal sentence recognition based on BERT-CNN text classification
CX Wan, B Li - The Journal of Supercomputing, 2022 - Springer
By studying the causality contained in financial texts, we can further reveal more potential
laws of economic activities, such as “factors promoting stable and healthy economic …
laws of economic activities, such as “factors promoting stable and healthy economic …
Joint event causality extraction using dual-channel enhanced neural network
J Gao, H Yu, S Zhang - Knowledge-Based Systems, 2022 - Elsevier
Abstract Event Causality Extraction (ECE) plays an essential role in many Natural Language
Processing (NLP), such as event prediction and dialogue generation. Recent research in …
Processing (NLP), such as event prediction and dialogue generation. Recent research in …
Automatic creation of acceptance tests by extracting conditionals from requirements: NLP approach and case study
Acceptance testing is crucial to determine whether a system fulfills end-user requirements.
However, the creation of acceptance tests is a laborious task entailing two major …
However, the creation of acceptance tests is a laborious task entailing two major …