A Survey on NLP Resources, Tools, and Techniques for Marathi Language Processing

Author:

Lahoti Pawan1ORCID,Mittal Namita1ORCID,Singh Girdhari1ORCID

Affiliation:

1. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

Abstract

Natural Language Processing (NLP) has been in practice for the past couple of decades, and extensive work has been done for the Western languages, particularly the English language. The Eastern counterpart, especially the languages of the Indian subcontinent, needs attention as not much language processing work has been done on these languages. Western languages are rich in dictionaries, WordNet, and associated tools, while Indian languages are lagging behind in this segment. Marathi is the third most spoken language in India and the 15th most spoken language worldwide. Lack of resources, complex linguistic facts, and the inclusion of prevalent dialects of neighbors have resulted in limited work for Marathi. The aim of this study is to provide an insight into the various linguistic resources, tools, and state-of-the-art techniques applied to the processing of the Marathi language. Initially, morphological descriptions of the Marathi language are provided, followed by a discussion on the characteristics of the Marathi language. Thereafter, for Marathi language, the availability of corpus, tools, and techniques to be used to develop NLP tasks is reviewed. Finally, gap analysis is discussed in current research and future directions for this new and dynamic area of research are listed that will benefit the Marathi Language Processing research community.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference120 articles.

1. Basil Abraham, Danish Goel, Divya Siddarth, Kalika Bali, Manu Chopra, Monojit Choudhury, Pratik Joshi, Preethi Jyoti, Sunayana Sitaram, and Vivek Seshadri. 2020. Crowdsourcing speech data for low-resource languages from low-income workers. In Proceedings of the 12th Language Resources and Evaluation Conference. 2819–2826.

2. Alekh Agarwal and Pushpak Bhattacharyya. 2006. Augmenting word net with polarity information on adjectives. In Proceedings of the 3rd International Wordnet Conference. 3–8.

3. JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages

4. A State-of-the-Art Survey on Deep Learning Theory and Architectures

5. A State-of-the-Art Survey on Deep Learning Theory and Architectures

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Systematic Review of Stemmers of Indian and Non-Indian Vernacular Languages;ACM Transactions on Asian and Low-Resource Language Information Processing;2024-01-15

2. Recognition and Transcription of Archaic Handwritten Modi Script Document: A Thought-Provoking Crucial Research Area;Communications in Computer and Information Science;2024

3. Breaking Barriers: Can Multilingual Foundation Models Bridge the Gap in Cross-Language Speech Emotion Recognition?;2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS);2023-11-21

4. A Generic Tool for Identification of Indo-Aryan Multi Word Expression;SN Computer Science;2023-10-14

5. User-aware multilingual abusive content detection in social media;Information Processing & Management;2023-09

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3