PAMR: Persian Abstract Meaning Representation Corpus-Reference-Cited by-同舟云学术

PAMR: Persian Abstract Meaning Representation Corpus

Published:2024-03-09 Issue:3 Volume:23 Page:1-20
ISSN:2375-4699
Container-title:ACM Transactions on Asian and Low-Resource Language Information Processing
language:en
Short-container-title:ACM Trans. Asian Low-Resour. Lang. Inf. Process.

Author:

Tohidi Nasim¹^ORCID,Dadkhah Chitra²^ORCID,Ganji Reza Nouralizadeh²^ORCID,Sadr Ehsan Ghaffari²^ORCID,Elmi Hoda²^ORCID

Affiliation:

1. Faculty of Computer Engineering, Artificial Intelligence Department, K. N. Toosi University of Technology, Tehran, Iran; University of Michigan, Ann Arbor, Michigan, USA

2. Faculty of Computer Engineering, Artificial Intelligence Department, K. N. Toosi University of Technology, Tehran, Iran

Abstract

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.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3638288

Reference56 articles.

1. Integrated semantic representation (ISR-Model): Syntax-independent model for natural language;Tohidi Nasim;Journal of Soft Computing and Information Technology,2023

2. O. Abend and A. Rappoport. 2013. UCCA: A semantics-based grammatical annotation scheme. In Proceedings of the 10th International Conference on Computational Semantics (IWCS ’13), 2013.

3. Banking meaning representations from treebanks;Butler Alistair;Linguistic Issues in Language Technology,2012

4. Alena Böhmová, Jan Hajič, Eva Hajičová, and Barbora Hladká. 2003. The Prague Dependency Treebank, Vol. 20. Springer, 103–127.