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Natural Language Processing for Detecting Forward Reference in a Document

机译:用于检测文档中前向引用的自然语言处理

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Meyer’s seven sins have been recognized as types of mistakes that a requirements specialist are often fallen to when specifying requirements. Such mistakes play a significant role in plunging a project into failure. Many researchers were focusing in ambiguity and contradiction type of mistakes. Other types of mistakes have been given less attentions. Those mistakes often happened in reality and may equally costly as the first two mistakes. This paper introduces an approach to detect forward reference. It traverses through a requirements document, extracts, and processes each statement. During the statement extraction, any terms that may reside in the statement is also extracted. Based on certain rules which utilize POS patterns, the statement is classified as a term definition or not. For each term definition, a term is added to a list of defined terms. At the same time, every time a new term is found in a statement, it is check against the list of defined terms. If it is not found, then the requirements statement is classified as statement with forward reference. The experimentation on 30 requirements documents from various domains of software project shows that the approach has considerably almost perfect agreement with domain expert in detecting forward reference, given 0.83 kappa index value.
机译:迈耶的七种罪过被认为是错误类型,需求专家在指定需求时经常会犯下这些错误。这些错误在使项目陷入失败中起着重要作用。许多研究人员专注于歧义和矛盾类型的错误。其他类型的错误已较少受到关注。这些错误经常在现实中发生,并且可能与前两个错误同样昂贵。本文介绍了一种检测前向参考的方法。它遍历需求文档,提取并处理每个语句。在语句提取过程中,还将提取可能驻留在语句中的任何术语。基于某些利用POS模式的规则,该语句是否分类为术语定义。对于每个术语定义,将一个术语添加到已定义术语的列表中。同时,每次在语句中找到新术语时,都会对照已定义术语列表进行检查。如果未找到,则将需求声明分类为具有前向参考的声明。对软件项目各个领域的30个需求文档进行的实验表明,在给定值为0.83 kappa索引值的情况下,该方法与领域专家在检测前向参考方面具有相当近乎完美的一致性。

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