This binary file is a model for a classification algorithm, that determines whether a given natural language sentence (from the requirements engineering context) contains a causal relation or not. The model utilizes transfer learning and is based on the popular BERT model. The model has been generated with the code referenced in https://github.com/fischJan/CiRA
We propose a neural network architecture for the task of causality classification. We claim that the...
“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answ...
This thesis studies the automatic recognition of implicit causal relations between clauses. Previous...
This repository contains the two models necessary to run the CiRA (Causality in Requirements Artifac...
[Context & motivation:] System behavior is often expressed by causal relations in requiremen...
Background: The detection and extraction of causality from natural language sentences have shown gre...
Causal relations in natural language (NL) requirements convey strong, semantic information. Automati...
The abundance of information on the internet has impacted the lives of people to a great extent. Peo...
Causal relations in natural language (NL) requirements convey strong, semantic information. Automati...
In this paper, we address the problem of extracting causal knowledge from text documents in a weakly...
Causal feature learning (CFL) (Chalupka et al., Proceedings of the Thirty-First Conference on Uncert...
Several supervised approaches have been proposed for causality identification by re-lying on shallow...
Causation relations are a pervasive feature of human language. Despite this, the automatic acquisi...
Causal inference is one of the most fundamental reasoning processes and one that is essential for qu...
The two fields of machine learning and graphical causality arose and are developed separately. Howev...
We propose a neural network architecture for the task of causality classification. We claim that the...
“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answ...
This thesis studies the automatic recognition of implicit causal relations between clauses. Previous...
This repository contains the two models necessary to run the CiRA (Causality in Requirements Artifac...
[Context & motivation:] System behavior is often expressed by causal relations in requiremen...
Background: The detection and extraction of causality from natural language sentences have shown gre...
Causal relations in natural language (NL) requirements convey strong, semantic information. Automati...
The abundance of information on the internet has impacted the lives of people to a great extent. Peo...
Causal relations in natural language (NL) requirements convey strong, semantic information. Automati...
In this paper, we address the problem of extracting causal knowledge from text documents in a weakly...
Causal feature learning (CFL) (Chalupka et al., Proceedings of the Thirty-First Conference on Uncert...
Several supervised approaches have been proposed for causality identification by re-lying on shallow...
Causation relations are a pervasive feature of human language. Despite this, the automatic acquisi...
Causal inference is one of the most fundamental reasoning processes and one that is essential for qu...
The two fields of machine learning and graphical causality arose and are developed separately. Howev...
We propose a neural network architecture for the task of causality classification. We claim that the...
“Causality” is a complex concept that is based on roots in almost all subject areas and aims to answ...
This thesis studies the automatic recognition of implicit causal relations between clauses. Previous...
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