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A handy Hindi phrasebook and guide to the Hindi language, Survival Hindi contains basic vocabulary necessary for getting around. This book contains all the necessary words and phrases for speaking Hindi in any kind of setting. Perfect for students, tourists, or business people learning Hindi or travelling to India, it also contains a beginner guide to the Indian language, allowing for a deeper understanding of Hindi than a typical Hindi phrasebook or Hindi dictionary. The book is broken into eight basic sections, covering everything from common Hindi expressions and key words to ordering in a restaurant. All Hindi words and phrases are written in Romanized form as well phonetically, making pronouncing Hindi a breeze. This phrasebook includes: Hundreds of useful Hindi words and expressions. An A-Z index with more than 1,000 words allowing the book to function as an English to Hindi dictionary. Romanized forms, phonetic spellings, and Hindi script (Devanagari) for all words and phrases. A concise background and history of the Hindi language. An introduction to the Hindi Alphabet. A pronunciation guide for Hindi. A guide to Hindi grammar.

Unsupervised Neural Machine Translation (UNMT) approaches have gained widespread popularity in recent times. Though these approaches show impressive translation performance using only monolingual corpora of the languages involved, these approaches have mostly been tried on high-resource European language pairs viz. English–French, English–German, etc. In this paper, we explore UNMT for 6 Indic language pairs viz., Hindi–Bengali, Hindi–Gujarati, Hindi–Marathi, Hindi–Malayalam, Hindi–Tamil, and Hindi–Telugu which are low-resource language pairs. We additionally perform experiments on 4 European language pairs viz., English–Czech, English–Estonian, English–Lithuanian, and English–Finnish. We observe that the lexical divergence within these language pairs plays a big role in the success of UNMT. In this context, we explore three approaches viz., (i) script conversion, (ii) unsupervised bilingual embedding-based initialization to bring the vocabulary of the two languages closer, and (iii) dictionary word substitution using a bilingual dictionary. We found that the script conversion using a simple rule-based system benefits language pairs that have high cognate overlap but use different scripts. We observe that script conversion combined with word substitution using a dictionary further improves the UNMT performance. We use a ground truth bilingual dictionary in our dictionary word substitution experiments, and such dictionaries can also be obtained using unsupervised bilingual embeddings. We empirically demonstrate that minimizing lexical divergence using simple heuristics leads to significant improvements in the BLEU score for both related and distant language pairs.

Transforming text from one language to another by using computer systems automatically or with little human interventions is known as Machine Translation System (MTS). Divergence among natural languages in a multilingual environment makes Machine Translation (MT) a difficult and challenging task. The purpose of this paper is to present a comprehensive survey of MTS in general and for English, Hindi and Sanskrit languages in particular. The state-of-the-art MT approach is Neural Machine Translation (NMT) which has been used by Google, Amazon, Facebook and Microsoft but it requires large corpus as well as high computing systems. The availability of MT language modeling tools, parsers data repositories and evaluation metrics has been tabulated in this article. The classification of MTS, evaluation methods and platforms has been done based on a well-defined set of criteria. The new research avenues have been explored in this survey article which will help in developing good quality MTS. Although several surveys have been done on MTS but none of them have followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach including tools and evaluation methods as done in this survey specifically for English, Hindi and Sanskrit languages.

Previous psycholinguistic studies have shown that bilinguals activate lexical items of both the languages during auditory and visual word processing. In this study we examined if Hindi–English bilinguals activate the orthographic forms of phonological neighbors of translation equivalents of the non target language while listening to words either in L1 or L2. We tracked participant’s eye movements as they looked at an array of written words that contained a phonological neighbor of the translation equivalent of a simultaneously presented spoken word. Participants quickly oriented their visual attention towards the phonological neighbor of the translation equivalent compared to the distractors, suggesting an activation of the spelling of the non-target lexicon via translation leading to further spreading activation of related words. Further, this parallel activation of the non target lexicon was seen in both L1–L2 and L2–L1 direction. These results suggest that different bilinguals can automatically activate the orthographic forms of the non-target lexicon via translation equivalents even when the languages in question do not share cognates and use different scripts.

NER assumes a key part in Information Extraction from reports (for example email), conversational information, and so forth. Many tongue handling applications, for example, data recovery, question responding to, and machine interpretation, depend on NER. It tends to be challenging to determine the ambiguities of lexical components utilized in a text arrangement. There is too much work has been already done in English language but there is a need to improve accuracy for the NER in Hindi language. In this research researcher are minimize chances of misclassification by using different classifier namely location, name, weather etc. BiLSTM Development of a NER framework for Indian languages is a similarly troublesome task. In this paper, Researcher have done the different research to contrast the aftereffects of NER and typical implanting and quick text implanting layers to examinations the exhibition of word installing with various bunch sizes to prepare the profound learning models. In this paper, Researcher have done the different examinations to contrast the consequences of NER and typical implanting and quick text installing layers to investigations the presentation of word inserting with various group sizes to prepare the profound learning models. The value of the precision of proposed system architecture is 76.13% which is way more than other system architectures. Also, the value of recall and F1-score of proposed system architecture is 71.49 and 74.26 respectively. So, by comparing proposed system architecture with existing SpaCy, CoreNLP and NLTK it is easy to conclude that proposed system architecture is reliable in all the sense.

Selection of the most suitable translation among all translation candidates returned by bilingual dictionary has always been quiet challenging task for any cross language query translation. Researchers have frequently tried to use word co-occurrence statistics to determine the most probable translation for user query. Algorithms using such statistics have certain shortcomings, which are focused in this paper. We propose a novel method for ambiguity resolution, named ‘two level disambiguation model’. At first level disambiguation, the model properly weighs the importance of translation alternatives of query terms obtained from the dictionary. The importance factor measures the probability of a translation candidate of being selected as the final translation of a query term. This removes the problem of taking binary decision for translation candidates. At second level disambiguation, the model targets the user query as a single concept and deduces the translation of all query terms simultaneously, taking into account the weights of translation alternatives also. This is contrary to previous researches which select translation for each word in source language query independently. The experimental result with English-Hindi cross language information retrieval shows that the proposed two level disambiguation model achieved 79.53% and 83.50% of monolingual translation and 21.11% and 17.36% improvement compared to greedy disambiguation strategies in terms of MAP for short and long queries respectively.

Spellchecker is software that analyzes possible misspellings in the text. It is the process of detecting and sometimes providing some suggestions for incorrectly spelled words in a text. If dictionary of spell checker is larger than higher is the error detection and error correction rate. Though considerable work has been done in English language but not much work has been done in regional languages of India including Punjabi and Hindi. Punjabi is the official language of Punjab state in India Punjabi is world's 12th most widely spoken language In Punjabi language, there is very small amount of work is completed in this region.. Hindi is the official language of 11 vowels and 33 consonants. Hindi is also the third most spoken language in the world. A few work is done in Punjabi-Hindi spell detection and correction field and it is not easy task to identify errors in Punjabi-Hindi text. The spell checker systems are online available but as not stand-alone applications. The only available spell checker for Punjabi is \"Akhar\" \"Raftaar\" and \"Sudhaar\". \"Akhar\" is paid software that is not available free for its use to everybody and \"Sudhaar\" spell checker is a desktop application Some paid Hindi spell checker software's are also online available. \"Hinspell\" & \"Hinkhoj\" are available spell checkers for Hindi language but a lot of improvement is needed. NLP (Natural language processing) is a field of computer science concerned with interaction between computer and human language. We have developed a combined spell checker and error correcting system for both Punjabi and Hindi Language. We used hybrid approach to implement the Spelling checking and Correcting System. The proposed system use hybrid approach which s a combination of various approaches like rule based approach, dictionary lookup approach , edit distance approach and N-Gram approach. Proposed system is tested with various inputs collected from different sources and results are found very accurate than that of existing system.

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-representation scheme. But given a particular interlingua, its adoption depends on its ability (a) to capture the knowledge in texts precisely and accurately and (b) to handle cross-language divergences. This paper studies the language divergence between English and Hindi and its implication to machine translation between these languages using the Universal Networking Language (UNL). UNL has been introduced by the United Nations University, Tokyo, to facilitate the transfer and exchange of information over the internet. The representation works at the level of single sentences and defines a semantic netlike structure in which nodes are word concepts and arcs are semantic relations between these concepts. The language divergences between Hindi, an Indo-European language, and English can be considered as representing the divergences between the SOV and SVO classes of languages. The work presented here is the only one to our knowledge that describes language divergence phenomena in the framework of computational linguistics through a South Asian language.

No one can deny that Hindi and its dialects have contributed scores of loan words and calques to Caribbean English Creole. How can this language with its own Devanagari script and complex phonology be transliterated into the Roman alphabet? How do Caribbean lexicographers deal with these challenges and limitations? This paper examines Hindi entries in bilingual Caribbean Hindi-English dictionaries, as well as in Caribbean, Jamaican, and Trinidadian English Creole lexical compilations. This paper focuses on the works of John Mendes, Martin Haynes, C. R. Ottley, Richard Allsopp, Rhona Baptiste, James Sookhoo, Parsram Thakur, Lester Orie, Kumar Mahabir, F. G. Cassidy, and R. B. Le Page. The study analyzes main entries of Hindi words, variant spelling and forms, syllabification, pronunciation, etymology, part-of-speech markers, and usage labels and notes. This is the first detailed study of this topic and it would be useful to all lexicographers as well as scholars, linguists, teachers, and students. [PUBLICATION ABSTRACT]

The Africans also joined the Indians in the Shi'ite Muslim commemoration of Hosay/Muharram which became a boisterous affair in the 1870s and 1880s (Wood 1968:153, Brereton 1979:183-4). [...]the process of inter-dialectal levelling and the evolution of Trinidad Bhojpuri was taking place, the Indians were learning French patois from the Africans (Gamble 1866:38-39). Spanish was in constant use in the European drygoods stores in Trinidad in the 1880s, but in an effort to increase sales by attracting new customers, clerks were advised to be familiar with other languages. Since the Coolies and Chinese have to come to the country, many clerks have managed to learn to speak a few words of Coolie as they term it, meaning of course Bengali, but in Tamil and Chinese, nothing can be done (Gamble 1886:39). Certain business establishments, catering specifically for an Indian market, have given Hindi names to their product brands, for example, Gowna ghee (a brand of clarified butter), Nariel (a brand of cooking coconut oil) and Chatak (a brand of spices and seasonings), Winer (1983:41) notes that the fact that Indie words like Hosay (a Muslim commemoration) and mandir (Hindu temple) appear in written texts without glosses indicate a presumption of general comprehensibility by monolingual Trinidad English speakers. Racist elements working in the weekly newspapers, for instance, rebaptized the ingenious politician and author of the book The Political Uses of Myth or Discrimination Rationalized (1993), Trevor Totaram Sudama, by referring to him as \"Toteeram\" - the compounded element fofee signifying a flaccid penis. [...]language was/is being used particularly by the African calypsonians as a weapon to destroy any symbol of Indian cultural retention and expression in Trinidad.