Name
Title | ||
|---|---|---|
Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks |
Abstract | ||
|---|---|---|
[Context:] Causal relations (e.g., If A, then B) are prevalent in functional requirements. For various applications of AI4RE, e.g., the automatic derivation of suitable test cases from requirements, automatically extracting such causal statements are a basic necessity. [Problem:] We lack an approach that is able to extract causal relations from natural language requirements in fine-grained form. S... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/REW53955.2021.00016 | 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) |
Keywords | DocType | ISBN |
Tensors,Codes,Conferences,Natural languages,Neural networks,Requirements engineering,Data mining | Conference | 978-1-6654-1898-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jannik Fischbach | 1 | 5 | 3.14 |
| Tobias Springer | 2 | 0 | 0.34 |
| Julian Frattini | 3 | 4 | 2.45 |
| Henning Femmer | 4 | 158 | 16.72 |
| Andreas Vogelsang | 5 | 83 | 31.23 |
| Daniel Mendez | 6 | 2 | 5.48 |