{"title":"PAMR： 波斯语抽象意义表征语料库","authors":"Nasim Tohidi, Chitra Dadkhah, Reza Nouralizadeh Ganji, Ehsan Ghaffari Sadr, Hoda Elmi","doi":"10.1145/3638288","DOIUrl":null,"url":null,"abstract":"<p>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. Besides, in some low-recourse languages, related corpora have been generated with less size. Although, till now to the best of our knowledge, there is not any AMR corpus for the Persian/Farsi language. Therefore, the aim of this paper 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 1020 Persian sentences and their related AMR which can be used in various natural language processing tasks. In this paper, to investigate the feasibility of using the corpus, we have applied it to two natural language processing tasks: Sentiment Analysis and Text Summarization.</p>","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":"12 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"PAMR: Persian Abstract Meaning Representation Corpus\",\"authors\":\"Nasim Tohidi, Chitra Dadkhah, Reza Nouralizadeh Ganji, Ehsan Ghaffari Sadr, Hoda Elmi\",\"doi\":\"10.1145/3638288\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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. Besides, in some low-recourse languages, related corpora have been generated with less size. Although, till now to the best of our knowledge, there is not any AMR corpus for the Persian/Farsi language. Therefore, the aim of this paper 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 1020 Persian sentences and their related AMR which can be used in various natural language processing tasks. In this paper, to investigate the feasibility of using the corpus, we have applied it to two natural language processing tasks: Sentiment Analysis and Text Summarization.</p>\",\"PeriodicalId\":54312,\"journal\":{\"name\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-01-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Asian and Low-Resource Language Information Processing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3638288\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Asian and Low-Resource Language Information Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3638288","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
摘要
抽象意义表示（AMR）是最常用、最著名的语义表示模型之一。近年来，这种表示法在自然语言处理任务中得到了大量应用。目前，英语和汉语都有大量的注释语料库。此外，在一些低词汇量语言中，相关的语料库也已生成，但规模较小。据我们所知，迄今为止还没有任何针对波斯语/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 指南创建一个波斯语 AMR（PAMR）语料库，并解决在这方面面临的各种挑战。这项研究的成果是一个包含 1020 个波斯语句子及其相关 AMR 的语料库，该语料库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，我们将其应用于两项自然语言处理任务：情感分析和文本总结。
PAMR: Persian Abstract Meaning Representation Corpus
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. Besides, in some low-recourse languages, related corpora have been generated with less size. Although, till now to the best of our knowledge, there is not any AMR corpus for the Persian/Farsi language. Therefore, the aim of this paper 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 1020 Persian sentences and their related AMR which can be used in various natural language processing tasks. In this paper, to investigate the feasibility of using the corpus, we have applied it to two natural language processing tasks: Sentiment Analysis and Text Summarization.
期刊介绍：
The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to:
-Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc.
-Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc.
-Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition.
-Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc.
-Machine Translation involving Asian or low-resource languages.
-Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc.
-Information Extraction and Filtering: including automatic abstraction, user profiling, etc.
-Speech processing: including text-to-speech synthesis and automatic speech recognition.
-Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc.
-Cross-lingual information processing involving Asian or low-resource languages.
-Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
