面向需求工程的自然语言处理技术分类
Classification of Natural Language Processing Techniques for Requirements Engineering
摘要：
将自然语言处理（NLP）技术应用于需求工程（RE）任务的研究跨越了40多年，从20世纪80年代的最初努力到最近的机器学习（ML）和深度学习（DL）技术的尝试。然而，尽管取得了进展，我们最近的调查表明，仍然缺乏系统的理解和组织RE中常用的NLP技术。我们认为，该行业面临的一个障碍是缺乏对NLP技术及其在RE任务中的使用的共享知识。在本文中，我们提出了我们的努力，综合和组织57个最常用的自然语言处理技术在RE。我们将这些NLP技术分为两种方式：第一，根据它们在典型管道中的NLP任务，第二，根据它们的语言分析水平。我们相信这两种分类方法是互补的，有助于更好地理解自然语言处理技术在RE和这样的理解是至关重要的，更好的自然语言处理工具的RE的发展。
英文摘要：
Research in applying natural language processing (NLP) techniques to requirements engineering (RE) tasks spans more than 40 years, from initial efforts carried out in the 1980s to more recent attempts with machine learning (ML) and deep learning (DL) techniques. However, in spite of the progress, our recent survey shows that there is still a lack of systematic understanding and organization of commonly used NLP techniques in RE. We believe one hurdle facing the industry is lack of shared knowledge of NLP techniques and their usage in RE tasks. In this paper, we present our effort to synthesize and organize 57 most frequently used NLP techniques in RE. We classify these NLP techniques in two ways: first, by their NLP tasks in typical pipelines and second, by their linguist analysis levels. We believe these two ways of classification are complementary, contributing to a better understanding of the NLP techniques in RE and such understanding is crucial to the development of better NLP tools for RE.
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。
