Abstract
A hybridised form of direct and rule-based language processing is used in this paper to present a Machine translation system from Sanskrit to Hindi. The divergence between Sanskrit and Hindi is also discussed in this paper, along with a proposition for how to handle it. Sanskrit-Hindi bilingual dictionaries, Grammatical Sanskrit corpus and a Sanskrit analyses rule base, have all been used in the projected system. The projected system's ability to access data from various data vocabularies and rule bases utilised in the system expansion has been improved by the usage of Elasticsearch technique. Additionally, a novel technique that builds a parse tree from the parsing table is presented in this paper. The system processes the input Sanskrit sentence using the parsing approach and the Context Free Grammar in normal form for Sanskrit language processing. No standard Sanskrit-Hindi Grammatical corpora available for Machine Translation which is designed and developed in the proposed work. The specific language sentence is produced using the Grammatical corpora and bilingual dictionaries. The proposed system achieved a Bilingual Evaluation Understudy (BLEU) score of 51.6 percent after being tested using Python's natural language toolkit API. The proposed system performs better than current systems when compared to cutting-edge systems, according to the comparison.
- Kak SC. 1987. The paninian approach to natural language processing. Int J Approx Reason 1(1), 117–130.Google ScholarCross Ref
- Briggs R. 1985. Knowledge representation in Sanskrit and artificial intelligence. AI Mag 6(1):32.Google Scholar
- Bahadur P, Jain A, and Chauhan DS. 2011. English to Sanskrit machine translation. In Proceedings of the international conference & workshop on emerging trends in technology. ACM, 641–645.Google Scholar
- Mishra V, and Mishra RB. 2008. Study of example-based English to Sanskrit machine translation. J Res Dev Comput Sci Eng, 37, 43–54.Google Scholar
- Mishra V, and Mishra RB. 2009. ANN and rule-based model for English to Sanskrit machine translation. INFOCOMP J Comput Sci 9(1), 80–89.Google Scholar
- Bahadur P, Jain AK, and Chauhan DS. 2012. Etrans-A complete framework for English to Sanskrit machine translation. International Journal of Advanced Computer Science and Applications (IJACSA) from international conference and workshop on emerging trends in technology. Citeseer, 52–59.Google Scholar
- Lewis MP, Simons GF, and Fennig CD. 2015. Ethnologue: languages of Ecuador. SIL International, Dallas.Google Scholar
- Mallikarjun B. 2010. Patterns of Indian multilingualism. In: Strength for today and bright hope for tomorrow, vol 10, no 6, 1–18.Google Scholar
- Dorr BJ, Hovy EH, and Levin LS. 2004. Natural language processing and machine translation encyclopaedia of language and linguistics, (ELL2). Machine translation: interlingual methods. In Proceeding international conference of the world congress on engineering.Google Scholar
- Dorr Bonnie J. 1994. Machine translation divergences: a formal description and proposed solution. Comput Linguist 20(4), 597–633.Google Scholar
- Goyal P, and Sinha RMK. 2009. Translation divergence in English– Sanskrit–Hindi language pairs. In International sanskrit computational linguistics symposium. Springer, 134–143.Google Scholar
- Shukla, P., Shukl, D., and Kulkarni, A. 2010. Vibhakti Divergence between Sanskrit and Hindi. In Jha, G.N. (eds) Sanskrit Computational Linguistics. ISCLS 2010. Lecture Notes in Computer Science(), vol 6465. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17528-2_15Google Scholar
- Goyal V, and Lehal GS. 2010. Web based Hindi to Punjabi machine translation system. J Emerg Technol Web Intell 2(2), 148–151.Google Scholar
- Dubey P. 2013. Machine translation system for Hindi–Dogri language pair. In 2013 international conference on machine intelligence and research advancement (ICMIRA). IEEE, 422–425.Google ScholarCross Ref
- Dubey P. 2019. The Hindi to Dogri machine translation system: grammatical perspective. Int J Inf Technol 11(1), 171–182.Google Scholar
- Narayana VN. 1994. Anusarak: a device to overcome the language barrier. PhD thesis, Department of CSE, IIT Kanpur.Google Scholar
- Bharati A, Chaitanya V, Kulkarni AP, and Sangal R. 1997. Anusaaraka machine translation in stages. VIVEK-Bombay 10, 22–25.Google Scholar
- Bharati RM, Sankar B, Reddy P, Sharma DM, and Sangal R. 2003. Machine translation: the shakti approach. Pre-conference tutorial. ICON-2003.Google Scholar
- Josan GS, and Lehal GS. 2008. A Punjabi to Hindi machine translation system. In 22nd international conference on computational linguistics: demonstration papers. Association for Computational Linguistics, 157–160.Google Scholar
- Rajan R, Sivan R, Ravindran R, and Soman KP. 2009. Rule based machine translation from English to Malayalam. In ACT’09. International conference on advances in computing, control, & telecommunication technologies, IEEE, 439–441.Google Scholar
- Goyal P, and Sinha RMK. 2009. A study towards design of an English to Sanskrit machine translation system. Sanskrit computational linguistics. Springer, 287–305.Google Scholar
- Pathak GR, and Godse SP. 2010. English to Sanskrit machine translation using transfer approach. In International conference on methods and models in science and technology. American Institute of Physics, Pune, 122–126.Google Scholar
- Mishra V, and Mishra RB. 2012. English to Sanskrit machine translation system: a rule-based approach. Int J Adv Intell Paradig 4(2), 168–184.Google ScholarDigital Library
- Reddy MV, and Hanumanthappa M. 2013. Indic language machine translation tool: English to Kannada/Telugu. In Multimedia processing, communication and computing applications. Springer, 35–49. https://doi.org/10.1007/978-81- 322-1143-3_4Google Scholar
- Jayan V, and Bhadran VK. 2014. Anglabharati to Anglamalayalam: an experience with English to Indian language machine translation. In 2014 international conference on contemporary computing and informatics (IC3I). IEEE, 282–287.Google Scholar
- Desai P, Sangodkar A, and Damani OP. 2014. A domain-restricted, rule based, English–Hindi machine translation system based on dependency parsing. In Proceedings of the 11th international conference on natural language processing, 177–185.Google Scholar
- Balyan R, and Chatterjee N. 2015. Translating noun compounds using semantic relations. Comput Speech Lang 32(1), 91–108.Google ScholarDigital Library
- Aasha VC, and Ganesh A. 2015. Machine translation from English to Malayalam using transfer approach. In 2015 international conference on advances in computing, communications and informatics (ICACCI). IEEE, 1565–1570.Google Scholar
- Sridhar R, Sethuraman P, and Krishnakumar K. 2016. English to Tamil machine translation system using universal networking language. Sa¯dhana¯ 41(6), 607–620.Google ScholarCross Ref
- Sinha R, sivaraman KS, Agrawal A, Jain R, Srivastava R, and Jain A. 1995. Anglabharti: a multilingual machine aided translation project on translation from English to Indian languages. In IEEE international conference on systems, man and cybernetics, 1995. Intelligent systems for the 21st century, vol 2. IEEE, 1609–1614.Google Scholar
- Darbari H. 1999. Computer-assisted translation system—an Indian perspective. In Machine translation summit VII, 13th–17th September, 80–85.Google Scholar
- Dave S, Parikh J, and Bhattacharyya P. 2001. Interlingua-based English-Hindi machine translation and language divergence. Mach Transl 16(4), 251–304.Google ScholarDigital Library
- Singh S, Dalal M, Vachani V, Bhattacharyya P, and Damani OP. 2007. Hindi generation from interlingua. In Proceedings of machine translation summit, 1–8.Google Scholar
- Choudhary A, and Singh M. 2009. Gb theory-based Hindi to English translation system. In 2nd IEEE international conference on computer science and information technology, ICCSIT 2009. IEEE, 293–297.Google Scholar
- Christopher M, and Rao UM. 2010. IL-ILMT sampark: a hybrid machine translation system. In 32nd all India conference of linguistics (AICL32). Lucknow University, 69–75.Google Scholar
- Batra KK, and Lehal GS. 2010. Rule based machine translation of noun phrases from Punjabi to English. Int J Comput Sci Issues 7(5), 409–413.Google Scholar
- Batra KK, and Lehal GS. 2011. Automatic translation system from Punjabi to English for simple sentences in legal domain. Int J Trans 23(1), 79–98.Google Scholar
- Kumar P, and Sharma RK. 2012. Punjabi to unl enconversion system. Sadhana 37(2), 299–318.Google ScholarCross Ref
- Parteek Kumar, and Rajendra Kumar Sharma. 2013. Punjabi deconverter for generating Punjabi from universal networking language. J Zhejiang Univ Sci C 14(3), 179–196.Google ScholarCross Ref
- Udupa UR, and Faruquie TA. 2005. An English–Hindi statistical machine translation system. In Su KY, Tsujii J, Lee JH, Kwong OY (eds) Natural language processing–IJCNLP 2004. IJCNLP 2004. Lecture notes in computer science, vol 3248. Springer, Berlin, Heidelberg, 254–262. https://doi.org/10.1007/978-3- 540-30211-7_27Google Scholar
- Antony PJ. 2013. Machine translation approaches and survey for Indian languages. Int J Comput Linguist Chin Lang Process 18(1), 47–78.Google Scholar
- Garje GV, and Kharate GK. 2013. Survey of machine translation systems in India. Int J Nat Lang Comput (IJNLC) 2(4), 47–67.Google ScholarCross Ref
- Sinha RMK. 2004. An engineering perspective of machine translation: anglabharti-ii and anubharti-ii architectures. In Proceedings of international symposium on machine translation, NLP and translation support system (iSTRANS-2004), 10–17.Google Scholar
- Jain R Sinha RMK, and Jain A. 2001. Anubharti-using hybrid example-based approach for machine translation. In: STRANS2001, IIT Kanpur, 20–32.Google Scholar
- Sinha RMK, and Thakur A. 2005. Machine translation of bi-lingual Hindi–English (Hinglish) text. In 10th Machine translation summit (MT Summit X), Phuket, Thailand, 149–156.Google Scholar
- Sachdeva K, Srivastava R, Jain S, and Sharma DM. 2014. Hindi to English machine translation: using effective selection in multimodel SMT. In LREC, 1807–1811.Google Scholar
- Dungarwal P, Chatterjee R, Mishra A, Kunchukuttan A, Shah R, and Bhattacharyya P. 2014. The IIT bombay Hindi–English translation system at WMT 2014. In: ACL 2014, 90-96.Google Scholar
- Och FJ. 2007. Google translator. In Joint conference on empirical methods in natural language processing and computational natural language learning. Prague. Association for Computational Linguistics, 858–867.Google Scholar
- Venkatapathy S, and Bangalore S. 2009. Discriminative machine translation using global lexical selection. ACM Trans Asian Lang Inf Process (TALIP) 8(2).Google Scholar
- Sharma N. 2011. English to Hindi statistical machine translation system. PhD thesis, Thapar University Patiala.Google Scholar
- Khan N, Anwar W, Bajwa UI, and Durrani N. 2013. English to Urdu hierarchical phrase-based statistical machine translation. In WSSANLP2013, Japan, 72–76.Google Scholar
- Ali A, Hussain A, Malik MK. 2013. Model for English–Urdu statistical machine translation. World Appl Sci 24, 1362–1367.Google Scholar
- Sheikh M, and Conlon S 2013. Application of machine translation in bilingual knowledge management. Int J Intercult Inf Manag 3(2), 123–137.Google Scholar
- Jawaid B, Kamran A, and Bojar O. 2014. English to Urdu statistical machine translation: establishing a baseline. In Proceedings of the Fifth workshop on south and southeast Asian natural language processing, 37–42.Google Scholar
- Naskar S, and Bandyopadhyay S. 2005. Use of machine translation in India: current status. AAMT J 16, 25–31.Google Scholar
- Badodekar S. 2003. Translation resources, services and tools for Indian languages. In Computer science and engineering department, Indian Institute of Technology.Google Scholar
- Saini TS, Lehal GS, and Kalra VS. 2008. Shahmukhi to Gurmukhi transliteration system. In 22nd international conference on on computational linguistics: demonstration papers. Association for Computational Linguistics, 177–180.Google Scholar
- Goyal V, and Lehal GS. 2011. Hindi to Punjabi machine translation system. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies: systems demonstrations. Association for Computational Linguistics, 1–6.Google Scholar
- Narayan R, Singh VP, and Chakraverty S. 2014. Quantum neural network based machine translator for Hindi to English. Sci World J 2014, 1–8. https://doi.org/10.1155/2014/485737Google ScholarCross Ref
- Sinha RMK, and Jain A. 2003. Anglahindi: an English to Hindi machine-aided translation system. In MT Summit IX, New Orleans, USA, 494–497.Google Scholar
- Sinha RMK. 2005. Integrating CAT and MT in Anglabharti-II architecture. In 10th EAMT conference, 235–244.Google Scholar
- Saha GK. 2005. The eb-anubad translator: a hybrid scheme. J Zhejiang Univ Sci A 6(10), 1047–1050.Google ScholarCross Ref
- NCST. 2008. Matra: an English to Hindi machine translation system. Technical report, NCST Mumbai.Google Scholar
- Shahnawaz A, and Mishra RB. 2011. Translation rules and ANN based model for English to Urdu machine translation. INFOCOMP J Comput Sci 10(3), 25–35.Google Scholar
- Shahnawaz, and Mishra RB. 2015. An English to Urdu translation model based on CBR ANN and translation rules. Int J Adv Intell Paradig 7(1), 1–23.Google ScholarDigital Library
- Jaideepsinh K, and Jatinderkumar S. 2016. Sanskrit machine translation systems: a comparative analysis. Int J Comput Appl 136, 1–4.Google Scholar
- Huet G. 2006. Shallow syntax analysis in Sanskrit guided by semantic nets constraints. In Proceedings of the 2006 international workshop on research issues in digital libraries. ACM.Google ScholarDigital Library
- Kulkarni A, Pokar S, and Shukl D. 2010. Designing a constraint-based parser for Sanskrit. In Sanskrit computational linguistics. Springer, 70–90.Google Scholar
- Kulkarni A. 2013. A deterministic dependency parser with dynamic programming for Sanskrit. In Proceedings of the second international conference on dependency linguistics (DepLing 2013), 157–166.Google Scholar
- Bhadra M, Singh SK, Kumar S, Agrawal M, Chandrasekhar R, Mishra SK, and Jha GN. 2009. Sanskrit analysis system (SAS). In Sanskrit computational linguistics. Springer, 116–133.Google Scholar
- Kumar A, Mittal V, and Kulkarni A. 2010. Sanskrit compound processor. In Sanskrit computational linguistics. Springer, 57–69.Google Scholar
- Bharati A, Kulkarni A. 2009. Anusaaraka: an accessor cum machine translator. Department of Sanskrit Studies, University of Hyderabad, 1–7.Google Scholar
- Aparna S. 2005. Sanskrit to English translator. In Language in India, vol 5.Google Scholar
- Upadhyay P, Jaiswal UC, and Ashish K. 2014. Transish: translator from Sanskrit to English-a rule based machine translation. Int J Curr Eng Technol 4(5), 2277–4106.Google Scholar
- Gopal M, Mishra D, and Singh DP. 2010. Evaluating tagsets for Sanskrit. In International sanskrit computational linguistics symposium. Springer, 150–161.Google Scholar
- Gopal M, and Jha GN. 2011. Tagging Sanskrit corpus using bis pos tagset. In International conference on information systems for Indian languages. Springer, 191–194.Google Scholar
- Gopal M, and Jha GN. 2007. Indian language part of speech tagger (IL-post). http://sanskrit.jnu.ac.in/corpora/tagset.jsp. Accessed 24 Aug 2022Google Scholar
- Chandershekhar R, Jha GN 2007. Part-of-speech tagging for Sanskrit. PhD thesis, Special Centre for Sanskrit Studies, JNU Delhi. http://sanskrit.jnu.ac.in/corpora/JNU-Sanskrit-Tagset.htmGoogle Scholar
- Sitender Bawa S. 2018. Sansunl: A Sanskrit to UNL enconverter system. IETE J Res. https://doi.org/10.1080/03772063.2018. 1528187Google Scholar
- Younger DH. 1967. Recognition and parsing of context-free languages in time n3. Inf Control 10(2), 189–208.Google ScholarCross Ref
- Li T, and Alagappan D. 2006. A comparison of CYK and earley parsing algorithms. In ICAR-CNR, 1–5.Google Scholar
- Papineni K, Roukos S, Ward T, and Zhu W-J. 2002. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 311–318.Google Scholar
- LDC. 2005. Linguistic data annotation specification: assessment of adequacy and fluency in translations. revision 1.5. Technical report, Linguistic Data Consortium.Google Scholar
- Kumar P, and Sharma RK. 2012. UNL based machine translation system for Punjabi language. PhD thesis, Thapar University.Google Scholar
- K. M. Kavitha, V. Naik, S. Angadi, S. Satish and S. Nayak. 2020. Hybrid Approaches for Augmentation of Translation Tables for Indian Languages. In 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 965-970. doi: 10.1109/ICMLA51294.2020.00157.Google Scholar
- Agrawal, P., and Jain, L. 2018. Anuvaadika: Implementation of Sanskrit to Hindi Translation Tool Using Rule-Based Approach. Recent Advances in Computer Science and Communications, 13(6), 1136–1151. https://doi.org/10.2174/2213275912666181226155829.Google ScholarCross Ref
- http://www.business-standard.com/article/current-affairs/hindiinternet-users-estimated-at-60-million-in-india-survey116020400922_1.htmlGoogle Scholar
- Singh, M., Kumar, R., and Chana, I. 2020. Corpus based Machine Translation System with Deep Neural Network for Sanskrit to Hindi Translation. Procedia Computer Science, 167(2019), 2534–2544. https://doi.org/10.1016/j.procs.2020.03.306Google ScholarCross Ref
- Kaka-Khan, K. Mikael. 2018. English to Kurdish Rule-based Machine Translation System. UHD Journal of Science and Technology, 2(2), 32–39. https://doi.org/10.21928/uhdjst.v2n2y2018.pp32-39Google ScholarCross Ref
- Mukta, A. P., Mamun, A. A., Basak, C., Nahar, S., and Arif, M. F. H. 2019. A Phrase-Based Machine Translation from English to Bangla Using Rule-Based Approach. In 2nd International Conference on Electrical, Computer and Communication Engineering, ECCE 2019, 1–5. https://doi.org/10.1109/ECACE.2019.8679456Google Scholar
- Singh, M., Kumar, R., and Chana, I. 2019. Neuro-FGA Based Machine Translation System for Sanskrit to Hindi Language. In International Conference on Innovative Sustainable Computational Technologies, CISCT 2019. https://doi.org/10.1109/CISCT46613.2019.9008136.Google Scholar
- Goyal, V., and Sharma, D. M. 2019. The IIIT-H Gujarati-English Machine Translation System for WMT19. 2(1), 191–195. https://doi.org/10.18653/v1/w19-5316.Google Scholar
- Vikrant Goyal and Dipti Misra Sharma. 2019. LTRC-MT Simple and Effective Hindi-English Neural Machine Translation Systems at WAT 2019. In Proceedings of the 6th Workshop on Asian Translation,137–140.Google ScholarCross Ref
- Koul, N., and Manvi, S. S. 2021. A proposed model for neural machine translation of Sanskrit into English. International Journal of Information Technology International Journal of Information Technology (Singapore), 13(1), 375–381. https://doi.org/10.1007/s41870-019-00340-8.Google ScholarCross Ref
- Mujadia, V., and Sharma, D. 2020. NMT based Similar Language Translation for Hindi - Marathi. Proceedings of the Fifth Conference on Machine Translation, 414–417. https://aclanthology.org/2020.wmt-1.48.Google Scholar
- Laskar, S. R., Pakray, P., and Bandyopadhyay, S. 2021. Neural Machine Translation for Low Resource Assamese–English. Lecture Notes in Networks and Systems, 170 LNNS(May), 35–44. https://doi.org/10.1007/978-981-33-4084-8_4.Google Scholar
- Chauhan, Shweta, Saxena, Shefali and Daniel, Philemon. 2021. Monolingual and Parallel Corpora for Kangri Low Resource Language.Google Scholar
- Rahul, L., Meetei, L.S., Jayanna, H.S. 2021. Statistical and Neural Machine Translation for Manipuri-English on Intelligence Domain. In Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 736. Springer, Singapore. https://doi.org/10.1007/978-981-33-6987-0_21.Google Scholar
- Donald Jefferson Thabah, N., and Purkayastha, B.S. 2021. Low Resource Neural Machine Translation from English to Khasi: A Transformer-Based Approach. In Maji, A.K., Saha, G., Das, S., Basu, S., Tavares, J.M.R.S. (eds) Proceedings of the International Conference on Computing and Communication Systems. Lecture Notes in Networks and Systems, vol 170. Springer, Singapore. https://doi.org/10.1007/978-981-33-4084-8_1.Google Scholar
- Salunkhe P, Kadam AD, Joshi S, Patil S, Thakore D, and Jadhav S. 2016. Hybrid machine translation for English to Marathi: a research evaluation in machine translation: (hybrid translator). In International conference on electrical, electronics, and optimization techniques (ICEEOT). IEEE, 924–931.Google Scholar
- Haroon, R. P., and Shaharban, T. A. 2016. Malayalam machine translation using hybrid approach. International Conference on Electrical, Electronics, and Optimization Techniques, 1013–1017. https://doi.org/10.1109/ICEEOT.2016.7754839.Google Scholar
- Dhariya, O., Malviya, S., and Tiwary, U. S. 2017. A hybrid approach for Hindi-English machine translation. International Conference on Information Networking, 389–394. https://doi.org/10.1109/ICOIN.2017.7899465Google Scholar
- Bengio Y, Ducharme R, Vincent P, and Jauvin C. 2003. A neural probabilistic language model. J Mach Learn Res 3 (Feb), 1137–1155.Google ScholarDigital Library
- Schwenk H. 2007. Continuous space language models. Comput Speech Lang 21 (3), 492–518.Google ScholarDigital Library
- Mikolov T. 2012. Statistical language models based on neural networks. Presentation at Google, Mountain View, 2nd April.Google Scholar
- Devlin J, Zbib R, Huang Z, Lamar T, and Schwartz R, Makhoul J. 2014. Fast and robust neural network joint models for statistical machine translation. In Proceedings of the 52nd annual meeting of the association for computational linguistics (vol 1: Long Papers), vol 1, 1370–1380.Google ScholarCross Ref
- Schwenk H, Rousseau A, and Attik M. 2012. Large, pruned or continuous space language models on a GPU for statistical machine translation. In Proceedings of the NAACL-HLT 2012 workshop: will we ever really replace the N-gram model? On the future of language modeling for HLT, Association for Computational Linguistics, 11–19.Google Scholar
- Wu Y, Yamamoto H, Lu X, Matsuda S, Hori C, and Kashioka H. 2012. Factored recurrent neural network language model in ted lecture transcription. In International workshop on spoken language translation (IWSLT).Google Scholar
- De Gispert A, Iglesias G, and Byrne B. 2015. Fast and accurate preordering for SMT using neural networks. In Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, 1012–1017.Google Scholar
- Kanouchi S, Sudoh K, and Komachi M. 2016. Neural reordering model considering phrase translation and word alignment for phrase-based translation. In Proceedings of the 3rd workshop on Asian translation (WAT2016), 94–103.Google Scholar
- Singh, M., Kumar, R., and Chana, I. 2019. Improving Neural Machine Translation Using Rule-Based Machine Translation. In 7th International Conference on Smart Computing and Communications, 1–5. https://doi.org/10.1109/ICSCC.2019.8843685.Google Scholar
- Salunkhe P, Kadam AD, Joshi S, Patil S, Thakore D, and Jadhav S. 2016. Hybrid machine translation for English to Marathi: a research evaluation in machine translation: (hybrid translator). In International conference on electrical, electronics, and optimization techniques (ICEEOT). IEEE, 924–931.Google Scholar
- Nithya B, and Joseph S. 2013. A hybrid approach to English to Malayalam machine translation. Int J Comput Appl 81(8), 11–15.Google Scholar
- Kaur H, and Laxmi DV. 2013. A web-based English to Punjabi MT system for news headlines. Int J Adv Res Comput Sci Softw Eng 3(6), 1092–1094.Google Scholar
- Dhore M, Dixit S, and Karande J. 2011. Web page interface localisation in Devanagari for commercial interactive applications by enhancing basic functionality of apache server. Int J Comput Appl 18(4), 6–10.Google Scholar
- Chatterji S, Sonare P, Sarkar S, and Basu A. 2011. Lattice based lexical transfer in Bengali Hindi machine translation framework. In Proceedings of ICON-2011: 9th international conference on natural language processing.Google Scholar
- Shahnawaz Mishra R. 2015. An English to Urdu translation model based on CBR, ANN and translation rules. Int J Adv Intell Paradig 7(1), 1–23.Google Scholar
- Chatterji S, Roy D, Sarkar S, and Basu A. 2009. A hybrid approach for Bengali to Hindi machine translation. In 7th international conference on natural language processing, 83–91.Google Scholar
- Z. Guo, K. Yu, Z. Lv, K. -K. R. Choo, P. Shi, and J. J. P. C. Rodrigues. 2022. Deep Federated Learning Enhanced Secure POI Microservices for Cyber-Physical Systems. In IEEE Wireless Communications, vol. 29, no. 2, 22-29. doi: 10.1109/MWC.002.2100272.Google ScholarDigital Library
- Z. Guo, K. Yu, N. Kumar, W. Wei, S. Mumtaz, and M. Guizani. 2022. Deep Distributed Learning-based POI Recommendation Under Mobile Edge Networks. In IEEE Internet of Things Journal. doi: 10.1109/JIOT.2022.3202628.Google Scholar
- A. K. Sangaiah, D. V. Medhane, T. Han, M. S. Hossain, and G. Muhammad. 2019. Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial Informatics. In IEEE Transactions on Industrial Informatics, vol. 15, no. 7, 4189-4196. doi: 10.1109/TII.2019.2898174.Google ScholarCross Ref
- G. Jain, T. Mahara, S. C. Sharma, and A. K. Sangaiah. 2022. A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System. In IEEE Transactions on Computational Social Systems. doi: 10.1109/TCSS.2022.3187430.Google Scholar
Recommendations
A Sanskrit-to-English machine translation using hybridization of direct and rule-based approach
AbstractThe work in this paper presents a MTS from Sanskrit to English language using a hybridized form of direct and rule-based machine translation technique. This paper also discusses the language divergence among Sanskrit and English languages with a ...
Source language adaptation approaches for resource-poor machine translation
Most of the world languages are resource-poor for statistical machine translation; still, many of them are actually related to some resource-rich language. Thus, we propose three novel, language-independent approaches to source language adaptation for ...
Interlingua-based English–Hindi Machine Translation and Language Divergence
Interlingua and transfer-based approaches to machine translation have long been in use in competing and complementary ways. The former proves economical in situations where translation among multiple languages is involved, and can be used as a knowledge-...
Comments