Abstract
Natural Language Processing is the ability of a system to understand, interpret, and analyze spoken words, text files, etc. There have been plethora of models that have been developed for language processing which consist of rule-based approach, Neural Network approach, and Traditional Machine Learning. NLP has a wide range of applications, including speech recognition, machine translation, sentiment analysis, chatbots, and intelligent personal assistants. In recent years, NLP has made significant progress, thanks to the development of deep learning models, which have greatly improved the performance of NLP systems. To make the machine understand the speech, the machine should be able to understand the sentences, which can only be achieved when the sentences are well-structured, which is ensured by Grammar. In this paper, a survey is done on various natural language processing works that have been done for the Nepali language, different resources, and work available.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Goldberg Y (2017) Neural network methods for natural language processing. Synth Lect Human Language Technol 10(1):1–309
Chowdhary K (2020) Natural language processing. Fundamentals of artificial intelligence, 603–649
Verma MTSR (2018) Natural language processing (Nlp): a comprehensive study
Plisson J, Lavrac N, Mladenic D (2004) A rule based approach to word lemmatization. In: Proceedings of IS, Vol 3, pp 83–86
Shahi TB, Dhamala TN, Balami B (2013) Support vector machines based part of speech tagging for Nepali text. Int J Comput Appl 70(24)
Lamsal R (2020) A large scale Nepali text corpus. IEEEdataport. https://doi.org/10.21227/jxrd-d245
Duwal S, Bal BK (2019) Efforts in the Development of an Aug- mented English–Nepali Parallel Corpus. Technical report, Kathmandu University
Shahi TB, Sitaula C (2021) Natural language processing for Nepali text: a review. Artif Intell Rev, 1–29
Nemkul K, Shakya S (2021) English to Nepali sentence translation using recurrent neural network with attention. In: 2021 international conference on computing, communication, and intelligent systems (ICCCIS), pp 607–611. IEEE
Nemkul K, Shakya S (2021) Low resource English to Nepali sentence translation using RNN—long short-term memory with attention. In: Proceedings of international conference on sustainable expert systems, pp 649–657. Springer, Singapore
Tiedemann J (2012) Parallel data, tools and interfaces in OPUS. In: Lrec, Vol 2012, pp 2214–2218
Staudemeyer RC, Morris ER (2019) Understanding LSTM--a tutorial into long short-term memory recurrent neural networks. arXiv preprint arXiv:1909.09586
Reiter E (2018) A structured review of the validity of BLEU. Comput Linguist 44(3):393–401
Timilsina S, Gautam M, Bhattarai B (2022) NepBERTa: Nepali language model trained in a large corpus. In: Proceedings of the 2nd conference of the Asia-pacific chapter of the association for computational linguistics and the 12th international joint conference on natural language processing, pp 273–284
Nivre J (2015) Towards a universal grammar for natural language processing. In: International conference on intelligent text processing and computational linguistics, pp 3–16. Springer, Cham
Dhanalakshmi V, Rajendran S (2010) Natural language processing tools for tamil grammar learning and teaching. Int J Comput Appl, 0975–8887
Triana JG, De Castro R (2019) Grammars and multifactorial numbers. Global J Pure Appl Math 15(3):251–259
Klein D, Manning CD (2005) Natural language grammar induction with a generative constituent-context model. Pattern Recogn 38(9):1407–1419
Nivre J (2005) Dependency grammar and dependency parsing. MSI report 5133(1959):1–32
Debusmann R (2000) An introduction to dependency grammar. Hausarbeit fur das Hauptseminar Dependenzgrammatik SoSe 99(1):16
Khatiwada R (2009) Nepali. J Int Phon Assoc 39(3):373–380
Matthews D (2013) Course in Nepali. Routledge
Bista S, Khatiwada L, Keshari B (2004) Nepali lexicon development. PAN Localization, Working Papers, 2007, 311–15
Bal BK, Shrestha P, Pustakalaya MP (2004) Nepali spellchecker. PAN Localization Working Papers, 2007, 316–318
Yadava YP, Hardie A, Lohani RR, Regmi BN, Gurung S, Gurung A, ... Hall P (2008) Construction and annotation of a corpus of contemporary Nepali. Corpora 3(2):213–225
Bal BK (2004) Structure of Nepali grammar. PAN Localization, Madan Puraskar Pustakalaya, Kathmandu, Nepal, 332–396
Jurish B, Würzner KM (2013) Word and Sentence Tokenization with Hidden Markov Models. J Lang Technol Comput Linguist 28(2):61–83
Katam S (2014) The porter stemmer. Indiana State University
Jivani AG (2011) A comparative study of stemming algorithms. Int J Comp Tech Appl 2(6):1930–1938
Khyani D, Siddhartha BS, Niveditha NM, Divya BM (2021) An Interpretation of Lemmatization and Stemming in Natural Language Processing. J Univ Shanghai Sci Technol
Shrestha I, Dhakal SS (2021) Fine-grained part-of-speech tagging in Nepali text. Procedia Computer Science 189:300–311
Sitaula C (2013) A hybrid algorithm for stemming of Nepali text
Borah S, Choden U, Lepcha N (2017) Design of a morph analyzer for non-declinable adjectives of nepali language. In: Proceedings of the 2017 international conference on machine learning and soft computing, pp 126–130
Chhetri I, Dey G, Das SK, Borah S (2015) Development of a morph analyser for Nepali noun token. In: 2015 international conference on advances in computer engineering and applications, pp 984–987. IEEE
Jayakodi K, Bandara M, Meedeniya D (2016) An automatic classifier for exam questions with WordNet and Cosine similarity. In: 2016 Moratuwa engineering research conference (MERCon), pp 12–17. IEEE
Lu X (2014) Lexical annotation. In: Computational methods for corpus annotation and analysis, pp 39–65. Springer, Dordrecht
Anees AF, Shaikh A, Shaikh A, Shaikh S (2020) Survey paper on sentiment analysis: techniques and challenges. EasyChair2516–2314
Subba S, Paudel N, Shahi TB (2019) Nepali text document classification using deep neural network. Tribhuvan Univ J 33(1):11–22
Tripathi M (2021) Sentiment analysis of nepali covid19 tweets using nb svm and lstm. J Artif Intell 3(03):151–168
Nothman J, Qin H, Yurchak R (2018) Stop word lists in free open-source software packages. In: Proceedings of workshop for NLP open source software (NLP-OSS), pp 7–12
Fernández-González D, Gómez-Rodríguez C (2023) Dependency parsing with bottom-up hierarchical pointer networks. Inf Fusion 91:494–503
ArchitYajnik D (2015) Parsing techniques using Paninian framework on Nepali language. DJ J Eng Appl Math 1(1)
Rai P, Chatterji S (2022) Annotation projection-based dependency parser development for Nepali. Transactions on asian and low-resource language information processing
Chiche A, Yitagesu B (2022) Part of speech tagging: a systematic review of deep learning and machine learning approaches. J Big Data 9(1):1–25
Li H, Mao H, Wang J (2021) Part-of-speech tagging with rule-based data preprocessing and transformer. Electronics 11(1):56
Zheng X, Chen H, Xu T (2013) Deep learning for Chinese word segmentation and POS tagging. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 647–657
Marquez L, Padro L, Rodriguez H (2000) A machine learning approach to POS tagging. Mach Learn 39(1):59–91
Prasain B, Khatiwada LP, Bal BK, Shrestha P (2008) Part-of-speech Tagset for Nepali. Madan Puraskar Pustakalaya
Bal BK, Shrestha P (2004) A morphological analyzer and a stemmer for Nepali. PAN Localization, working papers, 2007, 324–31
Yajnik A (2017) Part of speech tagging using statistical approach for Nepali text. Int J Cognit Language Sci 11(1):76–79
Paul A, Purkayastha BS, Sarkar S (2015) Hidden Markov model based part of speech tagging for Nepali language. In: 2015 international symposium on advanced computing and communication (ISACC), pp 149–156. IEEE
Prabha G, Jyothsna PV, Shahina KK, Premjith B, Soman KP (2018) A deep learning approach for part-of-speech tagging in nepali language. In: 2018 international conference on advances in computing, communications and informatics (ICACCI), pp 1132–1136. IEEE
Mohit B (2014) Named entity recognition. In: Natural language processing of semitic languages, pp 221–245. Springer, Berlin, Heidelberg
Bam SB, Shahi TB (2014) Named entity recognition for nepali text using support vector machines. Intell Inf Manag
Dey A, Paul A, Purkayastha BS (2014) Named entity recognition for nepali language: a semi hybrid approach. Int J Eng Innov Technol (IJEIT) 3:21–25
Singh OM, Padia A, Joshi A (2019) Named entity recognition for nepali language. In: 2019 IEEE 5th international conference on collaboration and internet computing (CIC), pp 184–190. IEEE
Lee YS, Wu YC (2007) A robust multilingual portable phrase chunking system. Expert Syst Appl 33(3):590–599
Rupakheti P, Report on Nepali Computational Grammar Prajwal Rupakheti, Laxmi Prasad Khatiwada Bal Krishna Bal Madan Puraskar Pustakalaya Lalitpur, PatanDhoka, Nepal.
Hippisley AR (2010) Lexical analysis
Vo AD, Nguyen QP, Ock CY (2020) Semantic and syntactic analysis in learning representation based on a sentiment analysis model. Appl Intell 50(3):663–680
Chandra P, Udaar U (2015) Ergative case and verbal agreement: explaining dialectal variations in Nepali. Acta Linguistica 9(1)
Goddard C (2011) Semantic analysis: a practical introduction. Oxford University Press
Maulud DH, Zeebaree SR, Jacksi K, Sadeeq MAM, Sharif KH (2021) State of art for semantic analysis of natural language processing. Qubahan Acad J 1(2):21–28
Meera S, Geerthik S (2022) Natural language processing. Artificial intelligent techniques for wireless communication and networking, 139–153
Zhao L, Alhoshan W, Ferrari A, Letsholo KJ, Ajagbe MA, Chioasca EV, Batista-Navarro RT (2021) Natural language processing for requirements engineering: a systematic mapping study. ACM Comput Surv (CSUR) 54(3):1–41
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sharma, S., Sharma, K., Sen, B. (2023). A Comprehensive Study on Natural Language Processing, It’s Techniques and Advancements in Nepali Language. In: Borah, S., Gandhi, T.K., Piuri, V. (eds) Advanced Computational and Communication Paradigms . ICACCP 2023. Lecture Notes in Networks and Systems, vol 535. Springer, Singapore. https://doi.org/10.1007/978-981-99-4284-8_13
Download citation
DOI: https://doi.org/10.1007/978-981-99-4284-8_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4283-1
Online ISBN: 978-981-99-4284-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)