需求工程中结构推理的自然语言--综述
Natural Language in Requirements Engineering for Structure Inference -- An Integrative Review
摘要：
从文本中自动提取结构对于机器来说可能是困难的。然而，这些信息的启发可以为各种应用提供许多好处和机会。需求工程领域的好处也已经确定。为了评估已经完成的工作和目前可用的，手头的文件提供了一个综合性的审查有关自然语言处理（NLP）工具的需求工程。进行这项评估是为了为今后的工作奠定基础，并从现状中得出见解。为了进行回顾，描述了需求工程和NLP的历史，以及对超过136个NLP工具的评估。为了评估这些工具，确定了一套标准。其结果是，目前不存在允许直接/主要提取信息结构的开源方法，甚至闭源解决方案也显示出诸如监督或输入限制之类的限制，这消除了全自动和通用应用的可能性。因此，作者推断，目前的方法是不适用的，不同的方法是必要的。一种允许对算法、知识库和文本语料库进行单独管理的方法是正在追求的可能性。
英文摘要：
The automatic extraction of structure from text can be difficult for machines. Yet, the elicitation of this information can provide many benefits and opportunities for various applications. Benefits have also been identified for the area of Requirements Engineering. To evaluate what work has been done and is currently available, the paper at hand provides an integrative review regarding Natural Language Processing (NLP) tools for Requirements Engineering. This assessment was conducted to provide a foundation for future work as well as deduce insights from the stats quo. To conduct the review, the history of Requirements Engineering and NLP are described as well as an evaluation of over 136 NLP tools. To assess these tools, a set of criteria was defined. The results are that currently no open source approach exists that allows for the direct/primary extraction of information structure and even closed source solutions show limitations such as supervision or input limitations, which eliminates the possibility for fully automatic and universal application. As a results, the authors deduce that the current approaches are not applicable and a different methodology is necessary. An approach that allows for individual management of the algorithm, knowledge base, and text corpus is a possibility being pursued.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
