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Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
arXiv - CS - Information Retrieval Pub Date : 2021-07-21 , DOI: arxiv-2107.09980
Jannik Fischbach, Tobias Springer, Julian Frattini, Henning Femmer, Andreas Vogelsang, Daniel Mendez

[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. Specifically, existing approaches do not consider the combinatorics between causes and effects. They also do not allow to split causes and effects into more granular text fragments (e.g., variable and condition), making the extracted relations unsuitable for automatic test case derivation. [Objective & Contributions:] We address this research gap and make the following contributions: First, we present the Causality Treebank, which is the first corpus of fully labeled binary parse trees representing the composition of 1,571 causal requirements. Second, we propose a fine-grained causality extractor based on 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. Third, we disclose our open data sets as well as our code to foster the discourse on the automatic extraction of causality in the RE community.

中文翻译:

使用递归神经张量网络从自然语言需求中提取细粒度因果关系

[上下文:] 因果关系(例如,如果 A,则 B)在功能需求中很普遍。对于 AI4RE 的各种应用,例如从需求中自动推导出合适的测试用例,自动提取这样的因果陈述是基本的必要条件。[问题:] 我们缺乏一种能够以细粒度形式从自然语言需求中提取因果关系的方法。具体而言,现有方法不考虑原因和结果之间的组合。它们也不允许将原因和结果拆分为更细粒度的文本片段(例如,变量和条件),使得提取的关系不适用于自动测试用例推导。[目标和贡献:] 我们解决了这一研究差距并做出了以下贡献:首先,我们提出了因果树库,这是第一个完全标记的二叉分析树语料库,代表 1,571 个因果要求的组成。其次,我们提出了一种基于递归神经张量网络的细粒度因果关系提取器。我们的方法能够恢复用自然语言编写的因果陈述的组成,并在因果树库的评估中获得了 74% 的 F1 分数。第三,我们公开了我们的开放数据集以及我们的代码,以促进在 RE 社区中自动提取因果关系的讨论。我们的方法能够恢复用自然语言编写的因果陈述的组成,并在因果树库的评估中获得了 74% 的 F1 分数。第三,我们公开了我们的开放数据集以及我们的代码,以促进在 RE 社区中自动提取因果关系的讨论。我们的方法能够恢复用自然语言编写的因果陈述的组成,并在因果树库的评估中获得了 74% 的 F1 分数。第三,我们公开了我们的开放数据集以及我们的代码,以促进在 RE 社区中自动提取因果关系的讨论。
更新日期:2021-07-22
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