软件工程建模在英语动词（和诗歌）分类中的应用
Software Engineering Modeling Applied to English Verb Classification (and Poetry)
摘要：
在需求规格说明中，软件工程师创建所设想系统的文本描述，并使用通用建模语言（UML）和系统建模语言（SysML）等工具开发概念模型。一个这样的工具，称为FM，最近已开发作为输入-过程-输出（IPO）模型的扩展。IPO已被广泛用于许多跨学科的应用，并被描述为所有描述工具中最基本和最重要的一个。本文是对IPO过程的一次尝试。描述过程的基本方法是用动词。这种语言的使用对系统建模有着重要的意义，因为动词表达了所有事物的各种动作和运动。很明显，建模需要检查动词。因此，本文将英语动词作为了解过程的桥梁，而不是作为语言学分析，而是为了揭示过程的语义，特别是形成FM状态基础的五个动词：创建，处理，接收，释放和转移。本文主要讨论动词的分类，特别是如何对动词的动作进行图形化建模。从语言学的角度来看，根据一些研究人员的说法，需要进一步探索动词类的概念，以用于现实世界的任务，如机器翻译，语言生成和文档分类。因此，这种非语言学的研究可能会对语言学有所裨益。
英文摘要：
In requirements specification, software engineers create a textual description of the envisioned system as well as develop conceptual models using such tools as Universal Modeling Language (UML) and System Modeling Language (SysML). One such tool, called FM, has recently been developed as an extension of the INPUT-PROCESS-OUTPUT (IPO) model. IPO has been used extensively in many interdisciplinary applications and is described as one of the most fundamental and important of all descriptive tools. This paper is an attempt to understanding the PROCESS in IPO. The fundamental way to describe PROCESS is in verbs. This use of language has an important implication for systems modeling since verbs express the vast range of actions and movements of all things. It is clear that modeling needs to examine verbs. Accordingly, this paper involves a study of English verbs as a bridge to learn about processes, not as linguistic analysis but rather to reveal the semantics of processes, particularly the five verbs that form the basis of FM states: create, process, receive, release, and transfer. The paper focuses on verb classification, and specifically on how to model the action of verbs diagrammatically. From the linguistics point of view, according to some researchers, further exploration of the notion of verb classes is needed for real-world tasks such as machine translation, language generation, and document classification. Accordingly, this non-linguistics study may benefit linguistics.
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。
