One of the most used and well-known semantic representation models is Abstract Meaning Representation (AMR). This representation has had numerous applications in natural language processing tasks in recent years. Currently, for English and Chinese languages, large annotated corpora are available. In addition, in some low-resource languages, related corpora have been generated with less size; although, until now, to the best of our knowledge, there is not any AMR corpus for the Persian/Farsi language. Therefore, the aim of this article is to create a Persian AMR (PAMR) corpus via translating English sentences and adjusting AMR guidelines and to solve the various challenges that are faced in this regard. The result of this research is a corpus, containing 1,020 Persian sentences and their related AMR that can be used in various natural language processing tasks. In this article, to investigate the feasibility of using the corpus, we have applied it to two natural language processing tasks: Sentiment Analysis and Text Summarization.

最常用和众所周知的语义表示模型之一是抽象含义表示（AMR）。近年来，这种表示在自然语言处理任务中得到了广泛的应用。目前，对于英语和汉语，可以使用大型注释语料库。此外，在一些资源匮乏的语言中，生成的相关语料库规模较小；尽管到目前为止，据我们所知，还没有任何波斯语/波斯语的 AMR 语料库。因此，本文的目的是通过翻译英语句子和调整AMR指南来创建波斯语AMR（PAMR）语料库，并解决这方面面临的各种挑战。这项研究的成果是一个语料库，包含 1,020 个波斯语句子及其相关的 AMR，可用于各种自然语言处理任务。在本文中，为了研究使用该语料库的可行性，我们将其应用于两个自然语言处理任务：情感分析和文本摘要。