WordNet: a lexical database for English
Communications of the ACM
Design rationale: concepts, techniques, and use
Design rationale: concepts, techniques, and use
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Design Rationale Systems: Understanding the Issues
IEEE Expert: Intelligent Systems and Their Applications
Rationale Management in Software Engineering
Rationale Management in Software Engineering
Rationale-Based Software Engineering
Rationale-Based Software Engineering
Adapting svm for data sparseness and imbalance: A case study in information extraction
Natural Language Engineering
Argumentation mining: the detection, classification and structure of arguments in text
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Using multiple ontologies in information extraction
Proceedings of the 18th ACM conference on Information and knowledge management
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Automated Identification of LTL Patterns in Natural Language Requirements
ISSRE '09 Proceedings of the 2009 20th International Symposium on Software Reliability Engineering
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Experiences with text mining large collections of unstructured systems development artifacts at jpl
Proceedings of the 33rd International Conference on Software Engineering
Text Processing with GATE
| Hi-index | 0.00 |
The rationale for a software system captures the designers’ and developers’ intent behind the decisions made during its development. This information has many potential uses but is typically not captured explicitly. This paper describes an initial investigation into the use of text mining and parsing techniques for identifying rationale from existing documents. Initial results indicate that the use of linguistic features results in better precision but significantly lower recall than using text mining.