[1] BOJAR O, STRAňáK P, ZEMAN D. Data issues in English-To-Hindi machine translation[C]//Proceedings of the International Conference on Language Resources & Evaluation, DBLP, 2010: 1771-1777.
[2] BAKER P, HARDIE A, MCENERY T, et al. EMILLE: A 67 million word corpus of Indic languages: Data collection, markup and harmonisation[C]//Proceedings of LREC, Lancaster University, 2002: 819-827.
[3] KUNCHUKUTTAN A, MEHTA P, BHATTACHARYYA P. The IIT Bombay English-Hindi parallel corpus[C]//Proceedings of LREC, 2018.
[4] SRIRAM V. NLPTOOLS contest: Summary[C]//Proceedings of ICON NLP Tools Contest, Pune, India, 2008.
[5] YADAV K, MISHRA S, SRIVASTAVA A. A review paper on part of speech tagger for Hindi[J]. International Journal of Engineering Applied Sciences and Technology, 2020, 5(2): 525-530.
[6] GARG N, GOYAL V, PREET S. Rule based Hindi part of speech tagger[C]//Proceedings of COLING 2012, Demonstration Papers, Mumbai, 2012: 163-174.
[7] MODI D, NAIN N. Part-of-speech tagging of Hindi corpus using rule-based method[C]//Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Image Processing, Springer, New Delhi, 2016: 241-247.
[8] MODI D, NAIN N, NEHRA M. Part-of-speech tagging for Hindi corpus in poor resource scenario[J]. Journal of Multimedia Information System, 2018, 5(3): 147-154.
[9] SHARMA A, YADAV V. Approaches to part of speech tagging in Hindi language: A review[J]. International Journal of Advanced Science and Technology, 2020, 29(5): 283-291.
[10] SHRIVASTAVA M, BHATTACHARYYA P. Hindi POS tagger using naive stemming: Harnessing morphological information without extensive linguistic knowledge[C]//Proceedings of the 6th International Conference on Natural Language Processing, Pune, India, 2008: 1-8.
[11] JOSHI N, DARBARI H, MATHUR I. HMM based POS tagger for Hindi[C]// Proceeding of International Conference on Computer Science & Information Technology, 2013: 341-349
[12] DALAL A, NAGARAJ K, SAWANT U, et al. Hindi part-of-speech tagging and chunking: A maximum entropy approach[C]//Proceedings of NLPAI Machine Learning Workshop on Part of Speech and Chunking for Indian Languages, 2006: 1-5.
[13] AGARWAL H,AMNI A. Part of speech tagging and chunking with conditional random fields[C]// Proceedings of the International Workshop on Replication in Empirical Software, Department of Computer Science, Hyderabad: Springer, India, 2006: 1-4.
[14] 王连喜,钟准,丁曾强,等.融合深度神经网络与统计学习的印地语词性标注方法研究[J].湖南工业大学学报, 2020, 34(3): 17-22.
[15] MISHRA N, JAIN S. POS tagging of Hindi language using hybrid approach[C]// Proceedings of the International Conference on Next Generation Computing Technologies, Springer, Singapore, 2018, 827: 723-735.
[16] NARAYAN R, CHAKRAVERTY S, SINGH V. Neural network based parts of speech tagger for Hindi[C]//Proceedings of the International Conference on Advances in Control & Optimization of Dynamical Systems, 2014: 519-524.
[17] NARAYAN R, SINGH V, CHAKRAVERTY S. Quantum neural network based parts of speech tagger for Hindi[J]. International Journal of Advancements In technology, 2014, 5(2): 137-152.
[18] YOUSIF J, SAINI D. Hindi part-of-speech tagger based neural networks[J]. Journal of Computing, 2011, 3(2): 59-66.
[19] PATEL H. HinFlair: Pre-trained contextual string embeddings for POS tagging and text classification in the Hindi language[J]. arXiv preprint, arXiv: 2101.06949v1, 2021.
[20] SHARMA R, MORWAL S, AGARWAL B. Named entity recognition for Hindi language: A survey[J]. Journal of Discrete Mathematical Sciences and Cryptography, 2019, 22(4): 569-580.
[21] KAUR Y, KAUR E. Named entity recognition system for Hindi language using combination of rule based approach and list look up approach[J]. International Journal of Scientific Research and Management, 2015, 3(3): 2300-2306.
[22] SHAH D, BHADKA H. A survey on various approach used in named entity recognition for Indian languages[J]. International Journal of Computer Applications, 2017, 167(1): 11-18.
[23] SAHA S, MITRA P, SARKAR S. A comparative study on feature reduction approaches in Hindi and Bengali named entity recognition[J]. Knowledge-based Systems, 2012, 27: 322-332.
[24] SARKAR K. Hindi named entity recognition using system combination[J]. International Journal of Applied Pattern Recognition, 2018, 5(1): 11-39.
[25] BHATTACHARJEE K, SHIVA K, MEHTA S, et al. Named entity recognition: A survey for Indian languages[C]//Proceedings of the 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies, Kannur, Kerala, India, 2019: 217-220.
[26] ATHAVALE V, BHARADWAJ S, PAMECHA M, et al. Towards deep learning in Hindi NER: An approach to tackle the labelled data scarcity[J]. arXiv preprint arXiv: 1610.09756, 2016.
[27] SHARMA R, MORWAL S, AGARWAL B, et al. A deep neural network-based model for named entity recognition for Hindi language[J]. Neural Computing & Applications, 2020, 32: 16191-16203.
[28] GAYEN V, SARKAR K. An HMM based named entity recognition system for Indian languages: The JU system at ICON 2013[J]. arXiv preprint, arXiv: 1405.7397, 2014.
[29] MORWAL S, JAHAN N, CHOPRA D. Named entity recognition using hidden Markov model[J]. International Journal on Natural Language Computing, 2012, 1(4): 15-23.
[30] SRIVASTAVA S, SANGLIKAR M, KOTHARI D. Named entity recognition system for Hindi language: A hybrid approach[J]. International Journal of Computational Linguistics, 2011, 2(1): 10-23.
[31] EKBAL A, BANDYOPADHYAY S. A conditional random field approach for named entity recognition in Bengali and Hindi[J]. Linguistic Issues in Language Technology, 2009, 2(1): 1-44.
[32] SAHA S, SARKAR S, MITRA P. A hybrid feature set based maximum entropy Hindi named entity recognition[C]// Proceedings of the 3rd International joint Conference on Natural Language Processing, California, USA, 2008: 343-349.
[33] SAHA S, NARAYAN S, SARKAR S, et al. A composite kernel for named entity recognition[J]. Pattern Recognition Letters, 2010, 31(12): 1591-1597.
[34] BISWAS S, MISHRA M, SITANATH_BISWAS S, et al. A two stage language independent named entity recognition for Indian languages[J]. International Journal of Computer Science and Information Technologies, 2010, 1(4): 285-289.
[35] SINGH V, VIJAY D, AKHTAR S, et al. Named entity recognition for Hindi-English code-mixed social media text[C]// Proceedings of the 7th Named Entities Workshop, 2018: 27-35.
[36] BARATHI GANESH H, SOMAN K, RESHMA U, et al. Information extraction for conversational systems in Indian languages - Arnekt IECSIL[C]//Proceedings of the 10th Annual Meeting of the Forum for Information Retrieval Evaluation, ACM, New York, USA, 2018: 18-20.
[37] SAHA S, MAJUMDER M. Development of a Hindi named entity recognition system without using manually annotated training corpus[J]. Development, 2018, 1(13): 18-37.
[38] PALMER M, BHATT R, NARASIMHAN B, et al. Hindi syntax: Annotating dependency, lexical predicate argument structure, and phrase structure[C]//Proceedings of the 7th International Conference on Natural Language Processing, Macmillan Publisher, India, 2009: 14-17.
[39] BHARATI A, GUPTA M, YADAV V, et al. Simple Parser for Indian languages in a dependency framework[C]// Proceedings of the 3rd Linguistic Annotation Workshop, Suntec, Singapore, 2009: 162-165.
[40] GUPTA M, YADAV V, HUSAIN S, et al. Partial parsing as a method to expedite dependency annotation of a Hindi treebank[C]// Proceedings of the 7th International Conference on Language Resources and Evaluation, LREC, 2010: 1930-1935.
[41] GOUTAM R. Exploring self-training and co-training for Hindi dependency parsing using partial parses[C] // Proceedings of the International Conference on Asian Language Processing, Hanoi, Vietnam, 2012: 37-40.
[42] BHAT R, BHATT R, FARUDI A, et al. The Hindi/Urdu treebank project[M]//Ide, N., Pustejovsky, J. Handbook of Linguistic Annotation, Springer, Amsterdam, 2017: 659-697.
[43] BHAT R, BHAT I, SHARMA D. Improving transition-based dependency parsing of Hindi and Urdu by modeling syntactically relevant phenomena[J]. ACM Transactions on Asian and Low-resource Language Information Processing, 2017, 16(3): 1-35.
[44] SAHA A, SARKAR S. Enhancing neural network based dependency parsing using morphological information for Hindi[C]// Proceedings of the International Conference on Intelligent Text Processing & Computational Linguistics, Springer, Cham, 2018: 366-377.
[45] KHAPRA M, BHATTACHARYYA P, CHAUHAN S, et al. Domain specific iterative word sense disambiguation in a multilingual setting[C]//Proceedings of International Conference on Natural Language Processing, Macmillan Publishers, India, 2008: 1-11.
[46] SINGH S, SIDDIQUI T. Evaluating effect of context window size, stemming and stop word removal on Hindi word sense disambiguation [C]//Proceedings of the International Conference on Information Retrieval & Knowledge Management, Kuala Lumpur, 2012: 1-5.
[47] SINGH S, SIDDIQUI T. Role of semantic relations in Hindi word sense disambiguation[J]. Procedia Computer Science, 2015,45: 240-248.
[48] SHARMA P, JOSHI N. Knowledge-based method for word sense disambiguation by using Hindi WordNet[J]. Engineering, Technology & Applied Science Research, 2019,9(2): 3985-3989.
[49] JAIN A, LOBIYAL D. Fuzzy Hindi WordNet and word sense disambiguation using fuzzy graph connectivity measures[J]. ACM Transactions on Asian and Low-resource Language Information Processing, 2015, 15(2): 1-31.
[50] JAIN G, LOBIYAL D. Word sense disambiguation of Hindi text using fuzzified semantic relations and fuzzy Hindi WordNet[C]//Proceedings of the 9th International Conference on Cloud Computing, Data Science & Engineering, Noida, India, 2019: 494-497.
[51] DWIVEDI S, VIKRAM S. Word sense ambiguity in question sentence translation: A review[C]// Proceedings of the International Conference on Information and Communication Technology for Intelligent Systems, Springer, 2018, 84: 64-71.
[52] JAIN A, LOBIYAL D. A New approach for unsupervised word sense disambiguation in Hindi language using graph connectivity measures[J]. International Journal of Artificial Intelligence & Soft Computing, 2014, 4(4): 318-334.
[53] SINGH S, SIDDIQUI T. Role of Karaka relations in Hindi word sense disambiguation[J]. Journal of Information Technology Research, 2015, 8(3): 21-42.
[54] SAWHNEY R, KAUR A. A modified technique for word sense disambiguation using Lesk algorithm in Hindi language[C]//Proceedings of the International Conference on Advances in Computing, Communications and Informatics, New Delhi, 2014: 2745-2749.
[55] PATEL N, PATEL B, PARIKH R, et al. Hierarchical clustering technique for word sense disambiguation using Hindi WordNet[C]//Proceedings of the 5th Nirma University International Conference on Engineering, Ahmedabad, 2015: 1-5.
[56] MISHRA N, SIDDIQUI T. An investigation to semi supervised approach for Hindi word sense disambiguation [C]//Proceedings of the Trends in Innovative Computing Intelligent Systems Design, 2012,12: 126-130.
[57] PURI S, SINGH S. Hindi text document classification system using SVM and fuzzy: A survey[J]. International Journal of Rough Sets and Date Analysis, 2018, 5(4): 1-31.
[58] DIAB D, HINDI K. Using differential evolution for fine tuning nave Bayesian classifiers and its application for text classification[J]. Applied Soft Computing, 2016, 54: 183-199.
[59] JOSHI R, GOEL P, JOSHI R. Deep learning for Hindi text classification: A Comparison[C]//Proceedings of the International Conference on Intelligent Human Computer Interaction, Allahabad, India, 2019: 94-111.
[60] GUPTA S, KHADE N. BERT based multilingual machine comprehension in English and Hindi[J]. arXiv preprint, arXiv: 2011.03435,2020.
[61] CHAKRAVARTHY S, UMAPATHY A, BLACK A. Detecting entailment in code-mixed Hindi-English conversations[C]//Proceedings of the 6th Workshop on Noisy User-Generated Text, Association for Computational Linguistics, 2020: 165-170.
[62] VISHWAKARMA S, LAKHTARIA K, BHATNAGAR D, et al. Monolingual information retrieval using terrier: FIRE 2010 experiments based on N-gram indexing[J]. Procedia Computer Science, 2015, 57: 815-820.
[63] SHARMA V, MITTAL N. Refined stop-words and morphological variants solutions applied to Hindi-English cross-lingual information retrieval[J]. Journal of Intelligent & Fuzzy Systems, 2019, 36: 2219-222.
[64] JAGARLAMUDI J, KUMARAN A. Cross-lingual information retrieval system for Indian languages[C]// Proceedings of the Advances in Multilingual & Multimodal Information Retrieval, Workshop of the Cross-language Evaluation Forum, Clef, Budapest, Hungary, 2007: 80-87.
[65] CHAKMA K, DAS A. CMIR: A corpus for evaluation of code mixed information retrieval of Hindi-English tweets[J]. Computacion Y Sistemas, 2016, 20(3): 425-434.
[66] VARSHNEY S, BAJPAI J. Improving retrieval performance of English-Hindi based cross-language information retrieval[C]//Proceedings of the IEEE International Conference in MOOC, Innovation & Technology in Education, Jaipur, IEEE, 2013: 300-305.
[67] KATTA E, ARORA A. An improved approach to English-Hindi based cross language information retrieval system[C]//Proceedings of the 8th International Conference on Contemporary Computing, Noida, 2015: 354-359.
[68] CHANDRA G, DWIVEDI S. Query expansion for effective retrieval results of Hindi-English cross-lingual IR[J]. Applied Artificial Intelligence, 2019, 33(5-8): 567-593.
[69] BHATTACHARYA P, GOYAL P, SARKAR S. Using word embeddings for query translation for Hindi to English cross language information retrieval[J]. ACM Transactions on Asian and Low-resource Language Information Processing, 2018, 18(1): 1-27.
[70] KALYANI A, SAJJA P. A review of machine translation systems in India and different translation evaluation methodologies[J]. International Journal of Computer Applications, 2015, 121(23): 16-23.
[71] SRIVASTAVA J, SANYAL S, SRIVASTAVA A. Extraction of reordering rules for statistical machine translation[J]. Journal of Intelligent & Fuzzy Systems, 2019, 36(5): 4809-4819.
[72] JADOON N, ANWAR W, BAJWA U, et al. Statistical machine translation of Indian languages: A survey[J]. Neural Computing and Applications, 2019, 31(7): 2455-2467.
[73] MALL S, JAISWAL U. Developing a system for machine translation from Hindi language to English language[C]//Proceedings of the IEEE 4th International Conference on Computer and Communication Technology, IEEE, 2013: 79-87.
[74] KHAN S, USMAN I. A model for English to Urdu and Hindi machine translation system using translation rules and artificial neural network[J]. International Arab Journal of Information Technology, 2019, 16(1): 125-131.
[75] CHATTERJEE N, GUPTA S. Efficient phrase table pruning for Hindi to English machine translation through syntactic and marker-based filtering and hybrid similarity measurement[J]. Natural Language Engineering, 2019, 25(1): 171-210.
[76] GUPTA P, COSTA-JUSSA M, ROSSO P, et al. A deep source-context feature for lexical selection in statistical machine translation[J]. Pattern Recognition letters, 2016, 75(1): 24-29.
[77] KUMAR M, PREMJITH B, SINGH S, et al. An overview of the shared task on machine translation in Indian languages (MTIL)-2017[J]. Journal of Intelligent Systems, 2019, 28(3): 455-464.
[78] ARORA K, AGRAWAL S. Pre-processing of English-Hindi corpus for statistical machine translation[J]. Computación Y Sistemas. 2017, 21(4): 725-737.
[79] BANIKD, EKBAL A, BHATTACHARYYA P. Machine learning based optimized pruning approach for decoding in statistical machine translation[J]. IEEE Access, 2019, 7: 1736-1751.
[80] JOHNSON M, SCHUSTER M, LE Q, et al. Googles multilingual neural machine translation system: Enabling zero-shot translation[J]. arXiv: 1611.04558,2016.
[81] NAGARHALLI T, VAZE V, RANA N. A novel framework for neural machine translation of Indian-English languages[C]//Proceedings of the International Conference on Inventive Computation Technologies, Coimbatore, India, 2020: 676-682.
[82] NARAYAN R, CHAKRAVERTY S, SINGH V. Quantum neural network based machine translator for English to Hindi[J]. Applied Soft Computing, 2016, 38: 1060-1075.
[83] PREMJITH B, KUMAR M, SOMAN K. Neural machine translation system for English to Indian language translation using MTIL parallel corpus[J]. Journal of Intelligent Systems, 2019, 28(3): 387-398.
[84] SAINI S, SAHULA V. Neural machine translation for English to Hindi[J]. Journal of Intelligent & Fuzzy Systems, 2020, 39(2): 2071-2079.
[85] TIWARI G, SHARMA A, Sahotra A, et al. English-Hindi neural machine translation-LSTM Seq2Seq and ConvS2S[C]//Proceedings of the IEEE International Conference on Communication and Signal Processing, 2020: 871-875.
[86] BANIK D, EKBAL A, BHATTACHARYYA P, et al. Assembling translations from multi-engine machine translation outputs[J]. Applied Soft Computing, 2019, 78: 230-239.
[87] LIU Y, GU J, GOYAL N, et al. Multilingual denoising pre-training for neural machine translation[J]. arXiv preprint arXiv: 2001.08210,2020.
[88] JAWAHAR G, NAGOUDI E, ABDUL-MAGEED M, et al. Exploring text-to-text transformers for English to Hinglish machine translation with synthetic code-mixing[J]. arXiv preprint arXiv: 2105.08807, 2021.
[89] GAUTAM D, KODALI P, GUPTA K, et al. CoMeT: Towards code-mixed translation using parallel monolingual sentences[C]//Proceedings of the 5th Workshop on Computational Approaches to Linguistic Code-Switching, Association for Computational Linguistics, 2021: 47-55.
[90] GAUTAM D, GUPTA K, SHRIVASTAVA M. Translate and classify: Improving sequence level classification for English-Hindi code-mixed data[C]//Proceedings of the 5th Workshop on Computational Approaches to Linguistic Code-Switching, Association for Computational Linguistics, 2021: 15-25.
[91] SEKI K. Cross-lingual text similarity exploiting neural machine translation models[J]. Journal of Information Science, 2020,47(3): 016555152091267.
[92] SHARMA V, MITTAL N. An improvement in statistical machine translation in perspective of Hindi-English cross-lingual information retrieval[J]. Computation Sistemas, 2018, 22(4): 1277-1285.
[93] AKHTAR M, EKBAL A, BHATTACHARYYA P. Aspect based sentiment analysis in Hindi: Resource creation and evaluation[C]//Proceedings of the 10th International Conference on Language Resources and Evaluation, Portoro, Slove-nia,2016: 2703-2709.
[94] GARG K, LOBIYAL D. Hindi EmotionNet: Scalable emotion lexicon for sentiment classification of Hindi text[J]. ACM Transactions on Asian & Low-resource Language Information Processing, 2020, 19(4): 1-35.
[95] PUNDLIK S, DASARE P, KASBEKAR P, et al. Multiclass classification and class based sentiment analysis for Hindi language[C]//Proceedings of the International Conference on Advances in Computing, Communications and Informatics, Jaipur, 2016: 512-518.
[96] VANDANA J, SAVITHA R, DEEPA S, et al. A novel sentiment aware dictionary for multi-domain sentiment classification[J]. Computers and Electrical Engineering, 2018, 69(6): 585-597.
[97] NANDA C, DUA M, NANDA G. Sentiment analysis of movie reviews in Hindi language using machine learning[C]//Proceedings of the International Conference on Communication and Signal Processing, Chennai, 2018: 1069-1072.
[98] SARKAR K. Heterogeneous classifier ensemble for sentiment analysis of Bengali and Hindi tweets[J]. Sadhana, 2020, 45(196): 1-17.
[99] AKHTAR M, GARG T, EKBAL A. Multi-task learning for aspect term extraction and aspect sentiment classification[J]. Neurocomputing, 2020, 398: 247-257.
[100] SESHADRI S, MADASAMY A, PADANNAYIL S. Analyzing sentiment in Indian languages micro text using recurrent neural network[J]. IIOAB Journal, 2016(7): 313-318.
[101] SHALINI K, GANESH H, KUMAR M, et al. Sentiment analysis for code-mixed Indian social media text with distributed representation[C]//Proceedings of the International Conference on Advances in Computing, Communications and Informatics, Bangalore, 2018: 1126-1131.
[102] RANI S, KUMAR P. Deep learning based sentiment analysis using convolution neural network[J]. Arabian Journal for Science and Engineering, 2019, 44(4): 3305-3314.
[103] BHARGAVA R, ARORA S, SHARMA Y. Neural network-based architecture for sentiment analysis in Indian languages[J]. Journal of Intelligent Systems, 2019, 28(3): 361-375.
[104] JAMATIA A, SWAMY S, GAMBCK B, et al. Deep learning based sentiment analysis in a code-mixed English-Hindi and English-Bengali social media corpus[J]. International Journal on Artificial Intelligence Tools, 2020, 29(5): 2050014-1～2050014-27.
[105] IMPANA P, KALLIMANI J. Cross-lingual sentiment analysis for Indian regional languages [C]//Proceedings of the International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques, Mysuru, 2017: 1-6.
[106] VIJAY S, RAI V, GUPTA S, et al. Extractive text summarization in Hindi[C]//Proceedings of the International Conference on Asian Language Processing, Singapore, 2017: 318-321.
[107] YADAV D, KUMAR K. An improvised extractive approach to Hindi text summarization[C]//Proceedings of the 2nd International Conference on Information Systems Design and Intelligent Applications, Kalyani, India, 2015, 39: 291-300.
[108] KUMAR K, DIVAKAR Y, ARUN S. Graph based technique for Hindi text summarization[C]//Proceedings of the 2nd International Conference on Information Systems Design and Intelligent Applications, Kalyani, India, 2015, 39: 301-310.
[109] VERMA P, PAL S, OM H. A comparative analysis on Hindi and English extractive text summarization[J]. ACM Transactions on Asian and Low Resource Language Information Processing, 2019,18(3): 1-30.
[110] RANI R, LOBIYAL D. an extractive text summarization approach using tagged-LDA based topic modeling[J]. Multimedia Tools and Applications, 2020,(1): 1-31.
[111] SUNITHA C, JAYA A, GANESH A. A study on abstractive summarization techniques in Indian languages[J]. Procedia Computer Science, 2016, 87: 25-31.
[112] DALAL V, MALIK L. Semantic graph based automatic text summarization for Hindi documents using particle swarm optimization[C]//Proceedings of the 2nd International Conference on Information and Communication Technology for Intelligent Systems, Ahmedabad, India, 2018, 84: 284-289.
[113] SINGH S, KUMAR A, MANGAL A, et al. Bilingual automatic text summarization using unsupervised deep learning[C]//Proceedings of the International Conference on Electrical, Electronics, and Optimization Techniques, Chennai, 2016: 1195-1200.
[114] GULATI A, SAWARKAR S. A novel technique for multidocument Hindi text summarization[C]//Proceedings of the International Conference on Nascent Technologies in Engineering, Navi Mumbai, 2017: 1-6.