Relevant bibliographies by topics / Telugu Translations

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Academic literature on the topic 'Telugu Translations'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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Journal articles on the topic "Telugu Translations"

1

Irvine, Ann, and Chris Callison-Burch. "A Comprehensive Analysis of Bilingual Lexicon Induction." Computational Linguistics 43, no. 2 (June 2017): 273–310. http://dx.doi.org/10.1162/coli_a_00284.

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Bilingual lexicon induction is the task of inducing word translations from monolingual corpora in two languages. In this article we present the most comprehensive analysis of bilingual lexicon induction to date. We present experiments on a wide range of languages and data sizes. We examine translation into English from 25 foreign languages: Albanian, Azeri, Bengali, Bosnian, Bulgarian, Cebuano, Gujarati, Hindi, Hungarian, Indonesian, Latvian, Nepali, Romanian, Serbian, Slovak, Somali, Spanish, Swedish, Tamil, Telugu, Turkish, Ukrainian, Uzbek, Vietnamese, and Welsh. We analyze the behavior of bilingual lexicon induction on low-frequency words, rather than testing solely on high-frequency words, as previous research has done. Low-frequency words are more relevant to statistical machine translation, where systems typically lack translations of rare words that fall outside of their training data. We systematically explore a wide range of features and phenomena that affect the quality of the translations discovered by bilingual lexicon induction. We provide illustrative examples of the highest ranking translations for orthogonal signals of translation equivalence like contextual similarity and temporal similarity. We analyze the effects of frequency and burstiness, and the sizes of the seed bilingual dictionaries and the monolingual training corpora. Additionally, we introduce a novel discriminative approach to bilingual lexicon induction. Our discriminative model is capable of combining a wide variety of features that individually provide only weak indications of translation equivalence. When feature weights are discriminatively set, these signals produce dramatically higher translation quality than previous approaches that combined signals in an unsupervised fashion (e.g., using minimum reciprocal rank). We also directly compare our model's performance against a sophisticated generative approach, the matching canonical correlation analysis (MCCA) algorithm used by Haghighi et al. ( 2008 ). Our algorithm achieves an accuracy of 42% versus MCCA's 15%.
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Arnold, B. J., H. Du, S. Eremenco, and D. Cella. "Using the FACT-Neurotoxicity Subscale to evaluate quality of life in patients from across the globe." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 17032. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.17032.

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17032 Background: Translation of Patient Reported Outcomes measures is an essential component of research methodology in preparation for multinational clinical trials. One such measure is the FACT-Neurotoxicity Subscale (FACT-Ntx) which is aimed at the evaluation of quality of life of cancer patients suffering from neurotoxicity, a side effect of certain treatments. Methods: This study set out to linguistically validate the FACT-Ntx for use in Denmark, India, Lithuania and S. Africa. The sample consisted of 176 patients (96 males & 80 females), with varying cancer diagnoses and a mean age of 51 years, speaking 11 languages: Afrikaans (15), Danish (25), Gujarati (15), Hindi (15), Kannada (15), Lithuanian (15), Malayalam (15), Marathi (15), Punjabi (15), Tamil (15) and Telugu (16). The FACT-Ntx was translated using standard FACIT methodology. Patients diagnosed with cancer, at any stage, receiving any treatment experiencing neurotoxicity completed the respective translated version and participated in cognitive debriefing interviews to give their opinion on any problems with the translations or the content of the FACT-Ntx. Statistical analyses (descriptive statistics, one-way ANOVA and reliability analyses) were performed on the quantitative data. Participant comments were analyzed qualitatively. Results: The FACT-Ntx translations showed good reliability and linguistic validity. The internal consistency of all languages combined was .86. All items correlated at an acceptable level. The Ntx score differed across self-reported Performance Status Rating (PSR) groups (nonparametric Kruskal-Wallis test p<.0001). A nonparametric Generalized Linear Model (GLM) approach (with multiple comparison adjusted significance level 0.017) showed a difference between ‘PSR=0’ and ‘PSR=1’ (p=0.0002) and a difference between ‘PSR=0’ and ‘PSR=2’ (p<.0001), both with ‘PSR=0’ patients reporting less neurotoxicity. Conclusions: The FACT-Ntx has shown acceptable reliability and linguistic validity in 11 languages. The instrument has also shown adequate sensitivity in differentiating patients with no symptoms and normal activity from patients reporting some symptoms. We consider these translations acceptable for use in international research and clinical trials. No significant financial relationships to disclose.
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Raju, B. N. V. Narasimha, M. S. V. S. Bhadri Raju, and K. V. V. Satyanarayana. "Effective preprocessing based neural machine translation for English to Telugu cross-language information retrieval." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 2 (June 1, 2021): 306. http://dx.doi.org/10.11591/ijai.v10.i2.pp306-315.

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<span id="docs-internal-guid-5b69f940-7fff-f443-1f09-a00e5e983714"><span>In cross-language information retrieval (CLIR), the neural machine translation (NMT) plays a vital role. CLIR retrieves the information written in a language which is different from the user's query language. In CLIR, the main concern is to translate the user query from the source language to the target language. NMT is useful for translating the data from one language to another. NMT has better accuracy for different languages like English to German and so-on. In this paper, NMT has applied for translating English to Indian languages, especially for Telugu. Besides NMT, an effort is also made to improve accuracy by applying effective preprocessing mechanism. The role of effective preprocessing in improving accuracy will be less but countable. Machine translation (MT) is a data-driven approach where parallel corpus will act as input in MT. NMT requires a massive amount of parallel corpus for performing the translation. Building an English - Telugu parallel corpus is costly because they are resource-poor languages. Different mechanisms are available for preparing the parallel corpus. The major issue in preparing parallel corpus is data replication that is handled during preprocessing. The other issue in machine translation is the out-of-vocabulary (OOV) problem. Earlier dictionaries are used to handle OOV problems. To overcome this problem the rare words are segmented into sequences of subwords during preprocessing. The parameters like accuracy, perplexity, cross-entropy and BLEU scores shows better translation quality for NMT with effective preprocessing.</span></span>
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S, Phani Kumar, and Prasad Rao P. "Rule based Translation Surface for Telugu Nouns." International Journal of Cloud Computing 12, no. 5 (2023): 1. http://dx.doi.org/10.1504/ijcc.2023.10039014.

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Gaur, Albertine. "Śiva's warriors: the Basava Purāna of Pālkuriki Somanātha. Translated from the Telugu by Velcheru Narayana Rao assisted by Gene H. Roghair. (Princeton Library of Asian Translations.) pp. xviii, 321, i illus. Princeton, NJ, Princeton University Press, 1990. US $49.50." Journal of the Royal Asiatic Society 1, no. 3 (November 1991): 416–18. http://dx.doi.org/10.1017/s135618630000136x.

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Sindhu, D. V., and B. M. Sagar. "Dictionary Based Machine Translation from Kannada to Telugu." IOP Conference Series: Materials Science and Engineering 225 (August 2017): 012182. http://dx.doi.org/10.1088/1757-899x/225/1/012182.

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Lingam, Keerthi, E. Ramalakshmi, and Srujana Inturi. "English to Telugu Rule based Machine Translation System: A Hybrid Approach." International Journal of Computer Applications 101, no. 2 (September 18, 2014): 19–24. http://dx.doi.org/10.5120/17659-8474.

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Prasad, Bathaloori Reddy. "Classification of Analyzed Text in Speech Recognition Using RNN-LSTM in Comparison with Convolutional Neural Network to Improve Precision for Identification of Keywords." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 1097–108. http://dx.doi.org/10.47059/revistageintec.v11i2.1739.

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Aim: Text classification is a method to classify the features from language translation in speech recognition from English to Telugu using a recurrent neural network- long short term memory (RNN-LSTM) comparison with convolutional neural network (CNN). Materials and Methods: Accuracy and precision are performed with dataset alexa and english-telugu of size 8166 sentences. Classification of language translation is performed by the recurrent neural network where a number of the samples (N=62) and convolutional neural network were a number of samples (N=62) techniques, the algorithm RNN implies speech recognition that can be compared with convolutional is the second technique. Results and Discussion: RNN-LSTM from the dataset speech recognition, feature Telugu_id produce accuracy 93% and precision 68.04% which can be comparatively higher than CNN accuracy 66.11%, precision 61.90%. It shows a statistical significance as 0.007 from Independent Sample T-test. Conclusion: The RNN-LSTM performs better in finding accuracy and precision when compared to CNN.
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Vishwas, Hunsur Nagendra, Jillella Sandeep Reddy, Pradeep Kumar Katravath, Naveen Kumar Posanpally, Prasanna Kumar Bojja, and Harish Gattikoppula. "Translation and Validation of Telugu Version of Marital Satisfaction Scale (T-MSS)." Indian Journal of Pharmacy Practice 10, no. 1 (May 1, 2017): 50–58. http://dx.doi.org/10.5530/ijopp.10.1.11.

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Krishnamurthy, Parameswari. "Development of Telugu-Tamil Transfer-Based Machine Translation System: An Improvization Using Divergence Index." Journal of Intelligent Systems 28, no. 3 (July 26, 2019): 493–504. http://dx.doi.org/10.1515/jisys-2018-0214.

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Abstract Building an automatic, high-quality, robust machine translation (MT) system is a fascinating yet an arduous task, as one of the major difficulties lies in cross-linguistic differences or divergences between languages at various levels. The existence of translation divergence precludes straightforward mapping in the MT system. An increase in the number of divergences also increases the complexity, especially in linguistically motivated transfer-based MT systems. This paper discusses the development of Telugu-Tamil transfer-based MT and how a divergence index (DI) is built to quantify the number of parametric variations between languages in order to improve the success rate of MT. The DI facilitates MT in proposing where to put efforts for the given language pair to attain better and faster results. In addition, handling strategies of different types of divergences in a transfer-based approach to MT are discussed. The paper also includes the evaluation method and how an improvization takes place with the application of DI in MT.
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Dissertations / Theses on the topic "Telugu Translations"

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Rao, Durga Srinivasa T. "Problems of translating satire from english to telugu and vice versa: An evaluation." Thesis, 2004. http://hdl.handle.net/2009/858.

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Pillalamarri, Aravinda. "Kites in the air, bubbles on the water translation, context, and criticism of a Telugu novella by Tenneti Hema Lata /." 2000. http://catalog.hathitrust.org/api/volumes/oclc/44392223.html.

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Thesis (M.A.)--University of Wisconsin--Madison, 2000.
Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 117-121).
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Books on the topic "Telugu Translations"

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Ramaṇaprasād, Amaḷḷadinne Vēṅkaṭa. Telugu sāhityaṃlō Mr̥cchakaṭikaṃ. Hanumānjaṅkṣan: Bhāgīrathī Pracuraṇalu, 1993.

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Carr, M. W. Telugu sāmetalu =: A Selection of Telugu proverbs, translated and explained. Karnūlu: Bālasarasvatī Buk Ḍipō, 1989.

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The black rainbow: Dalit poems in Telugu. Hyderabad: Milinda Publications, 2000.

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1947 Santoshabad passenger and other stories: Translations of Telugu stories. New Delhi: Rupa & Co., 2010.

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Krishnamoorty, Dasu. 1947 Santoshabad passenger and other stories: Translations of Telugu stories. New Delhi: Rupa & Co., 2010.

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Gōvindarāju, Sītādēvi. A little lamp and other short stories from Telugu. Edited by Narasimha Rao Vemaraju. Hyderabad: Navya Sahiti Samiti, 1996.

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Purandaradāsa. Purandaradāsula padamulu: Telugulō. Anantapuram: Pāṇyaṃ Rāmaśēṣa Śāstri, 2010.

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Black lotus: Telugu dalit women's poetry. New Delhi: Adhyayan Publishers & Distributors, 2014.

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Ramēś, Sa Veṃ. Morasunāḍu katalu. Hōsuru: Kr̥ṣṇagiri Jillā Telugu Racayitala Saṅghaṃ, 2013.

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Saṅgaṃ, Toṇḍanāḍu Telugu Racayitala, ed. Toṇḍanāḍu kathalu. [Tirupati]: Toṇḍanāḍu Telugu Racayitala Saṅgaṃ, 2013.

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Book chapters on the topic "Telugu Translations"

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Reddy, Mallamma V., and M. Hanumanthappa. "Indic Language Machine Translation Tool: English to Kannada/Telugu." In Lecture Notes in Electrical Engineering, 35–49. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1143-3_4.

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Kameswara Rao, T., and T. V. Prasad. "Machine Translation of Telugu Singular Pronoun Inflections to Sanskrit." In Advances in Intelligent Systems and Computing, 293–306. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2734-2_30.

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"On the Process of Translation." In For the Lord of the Animals-Poems from The Telugu, IX—X. University of California Press, 1987. http://dx.doi.org/10.1525/9780520335950-001.

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Kumar, Raghvendra, Prasant Kumar Pattnaik, and Priyanka Pandey. "Conversion of Higher into Lower Language Using Machine Translation." In Web Semantics for Textual and Visual Information Retrieval, 92–107. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2483-0.ch005.

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This chapter addresses an exclusive approach to expand a machine translation system beginning higher language to lower language. Since we all know that population of India is 1.27 billion moreover there are more than 30 language and 2000 dialects used for communication of Indian people. India has 18 official recognized languages similar to Assamese, Bengali, English, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Malayalam, Manipuri, Marathi, Nepali, Oriya, Punjabi, Sanskrit, Tamil, Telugu, and Urdu. Hindi is taken as regional language and is used for all types of official work in central government offices. Commencing such a vast number of people 80% of people know Hindi. Though Hindi is also regional language of Jabalpur, MP, India, still a lot of people of Jabalpur are unable to speak in Hindi. So for production those people unswerving to know Hindi language we expand a machine translation system. For growth of such a machine translation system, used apertium platform as it is free/open source. Using apertium platform a lot of language pairs more specifically Indian language pairs have already been developed. In this chapter, develop a machine translation system for strongly related language pair i.e Hindi to Jabalpuriya language (Jabalpur, MP, India).
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Kumar K., Vimal, and Divakar Yadav. "Word Sense Based Hindi-Tamil Statistical Machine Translation." In Natural Language Processing, 410–21. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0951-7.ch021.

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Corpus based natural language processing has emerged with great success in recent years. It is not only used for languages like English, French, Spanish, and Hindi but also is widely used for languages like Tamil, Telugu etc. This paper focuses to increase the accuracy of machine translation from Hindi to Tamil by considering the word's sense as well as its part-of-speech. This system works on word by word translation from Hindi to Tamil language which makes use of additional information such as the preceding words, the current word's part of speech and the word's sense itself. For such a translation system, the frequency of words occurring in the corpus, the tagging of the input words and the probability of the preceding word of the tagged words are required. Wordnet is used to identify various synonym for the words specified in the source language. Among these words, the one which is more relevant to the word specified in source language is considered for the translation to target language. The introduction of the additional information such as part-of-speech tag, preceding word information and semantic analysis has greatly improved the accuracy of the system.
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Conference papers on the topic "Telugu Translations"

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Mukerjee, Amitabha, and Madan Mohan Dabbeeru. "Using Symbol Emergence to Discover Multi-Lingual Translations in Design." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-29216.

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Incorporating design knowledge into computational design requires “symbols” — but this term as used in knowledge-based models of design is a formal term, defined only in terms of other symbols. For most humans, symbols are [term : meaning] pairs that emerge while interacting with real designs. However, both the term and its interpretation vary considerably across design groups, particularly in today’s international cooperative design scenario. For translating symbols in design, one needs to incorporate the design context, which is since the actual design object and its characteristics form the most relevant part of the context. In this work, we consider an embodied symbols approach towards translation, where models corresponding to symbol semantics are discovered based on functional norms in a given design context. The functions are available as performance measures on a given task, and lead to low-dimensional characterizations (called image schema) that reveal inter-relations in the input space that must hold for functional validity. Some of these image schemas eventually acquire language labels and become symbols. Since different designers differ in experience and in language their symbols differ somewhat. Here we consider how independent language agents may map these low-dimensional characterizations (called chunks) to units of languages based on human commentary produced in the same context. We demonstrate how this process may work for the simple domain of insertion tasks and fits, and learn both the image schemas and the language labels in two different languages, English and Telugu.
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Suryakanthi, T., and Kamlesh Sharma. "Discourse Translation from English to Telugu." In the Third International Symposium. New York, New York, USA: ACM Press, 2015. http://dx.doi.org/10.1145/2791405.2791459.

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Ghosh, Siddhartha, Sujata Thamke, and Kalyani U.R.S. "Translation of Telugu-Marathi and Vice-Versa Using Rule Based Machine Translation." In Fourth International Conference on Advances in Computing and Information Technology. Academy & Industry Research Collaboration Center (AIRCC), 2014. http://dx.doi.org/10.5121/csit.2014.4501.

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Rao, T. Kameswara, and T. V. Prasad. "Machine Translation of Telugu plural pronoun declensions to Sanskrit." In 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2016. http://dx.doi.org/10.1109/icatcct.2016.7912012.

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Rao, T. Kameswara, M. Rajyalakshmi, and T. V. Prasad. "Handling incomplete verb conjugations of telugu in machine translation." In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017. http://dx.doi.org/10.1109/icbdaci.2017.8070815.

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"FOURTH WORLD LITERATURE WITH SPECIAL REFERENCE TO MUSLIM MINORITY TREND IN TELUGU LITERATURE." In 2nd National Conference on Translation, Language & Literature. ELK Asia Pacific Journals, 2015. http://dx.doi.org/10.16962/elkapj/si.nctll-2015.22.

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"REVEALING HISTORY THROUGH LYRICS: A STUDY ON CONTEMPORARY SELECT LYRICS IN TELUGU MOVIES." In 2nd National Conference on Translation, Language & Literature. ELK Asia Pacific Journals, 2015. http://dx.doi.org/10.16962/elkapj/si.nctll-2015.7.

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Lingam, Keerthi, E. Rama Lakshmi, and L. Ravi Theja. "Rule-based machine translation from English to Telugu with emphasis on prepositions." In 2014 International Conference on Networks & Soft Computing (ICNSC). IEEE, 2014. http://dx.doi.org/10.1109/cnsc.2014.6906669.

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Sindhu D.V and Sagar B.M. "A case study on linguistic divergences in Kannada-Telugu Machine Translation perceptive." In 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2016. http://dx.doi.org/10.1109/csitss.2016.7779448.

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"REGIONAL SHORT STORIES IN TRANSLATION FOR DEVELOPING LANGUAGE SKILLS OF EFL LEARNERS: A SELECTED PART OF SOWRIS’ TELUGU SHORT STORY ‘REMINISCENCE’." In 2nd National Conference on Translation, Language & Literature. ELK Asia Pacific Journals, 2015. http://dx.doi.org/10.16962/elkapj/si.nctll-2015.8.

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