Search Results for author:
Found 8 papers, 3 papers with code
Automatic ESG Assessment of Companies by Mining and Evaluating Media Coverage Data: NLP Approach and Tool
Second, we present our tool-supported approach called ESG-Miner capable of analyzing and evaluating headlines on corporate ESG-performance automatically.
Transfer Learning for Mining Feature Requests and Bug Reports from Tweets and App Store Reviews
In this paper, we investigate the potential of transfer learning (TL) for the classification of user comments.
CATE: CAusality Tree Extractor from Natural Language Requirements
Causal relations (If A, then B) are prevalent in requirements artifacts.
Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
Our approach is capable of recovering the composition of causal statements written in natural language and achieves a F1 score of 74 % in the evaluation on the Causality Treebank.
CiRA: A Tool for the Automatic Detection of Causal Relationships in Requirements Artifacts
Requirements often specify the expected system behavior by using causal relations (e. g., If A, then B).
Software Engineering
Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach
Our case study corroborates, among other things, that causality is, in fact, a widely used linguistic pattern to describe system behavior, as about a third of the analyzed sentences are causal.
Software Engineering
Towards Causality Extraction from Requirements
In this paper, we describe first steps towards building a new approach for causality extraction and contribute: (1) an NLP architecture based on Tree Recursive Neural Networks (TRNN) that we will train to identify causal relations in NL requirements and (2) an annotation scheme and a dataset that is suitable for training TRNNs.
Automated Generation of Test Models from Semi-Structured Requirements
[Contribution:] We make three contributions: (1) an algorithm for the automatic detection of semi-structured requirements descriptions in documents, (2) an algorithm for the automatic translation of the identified requirements into a CEG and (3) a study demonstrating that our proposed solution leads to 86 % time savings for test model creation without loss of quality.
