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Academic literature on the topic 'Translations from Telugu'

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Published: 25 July 2024
Last updated: 31 July 2024

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Journal articles on the topic "Translations from Telugu"

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K. V. V. Satyanarayana, M. S. V. S. Bhadri Raju, B. N. V. Narasimha Raju,. "BiLSTMs and BPE for English to Telugu CLIR." Journal of Electrical Systems 20, no. 3s (April 4, 2024): 2022–29. http://dx.doi.org/10.52783/jes.1798.

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A crucial component of Cross Lingual Information Retrieval (CLIR) is Neural Machine Translation (NMT). NMT performs a good job of transforming queries in the English language into Indian languages. This study focuses on the translation of English queries into Telugu. For translations, the NMT will make use of a parallel corpus. Due to a lack of resources in the Telugu language, it is exceedingly challenging to provide NMT with sizable parallel corpora. Thus, the NMT will encounter an issue known as Out of Vocabulary (OOV). Long Short-Term Memory (LSTM) with Byte Pair Encoding (BPE), which breaks up rare words into subwords and attempts to translate them to solve the OOV issue. Issues such as Named Entity Recognition (NER) continue to plague it. In sequence-to-sequence models, bidirectional LSTMs can solve certain NER challenges. Systems that need to be trained in both directions to recognize named entities can benefit from the use of Bidirectional LSTMs (BiLSTMs). The translation efficiency of NMT with BiLSTMs is significantly higher than normal LSTMs, as indicated by the accuracy metrics and Bilingual Evaluation Understudy (BLEU) score.
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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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Ashari, Erwin, Marida Hannum Harahap, and Shalehoddin Shalehoddin. "TRANSLATION TECHNIQUES USED BY ENGLISH THIRD SEMESTER STUDENTS OF UNIVERSITY OF RIAU KEPULAUAN." ANGLO-SAXON: Jurnal Ilmiah Program Studi Pendidikan Bahasa Inggris 13, no. 2 (December 30, 2022): 285–97. http://dx.doi.org/10.33373/as.v13i2.5002.

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Translation is the transferring meaning of the text from source language in which to equivalent text in target language that communicates similar messages by changing structure without changing meaning. The qualitative descriptive method as the research design of this study which aimed to find out translation techniques used by English third semester students in translating a narrative text entitled “Telaga Warna” from English into Indonesian and to describe the dominant technique used by the students in translating the text. The collecting of the data collected taken from the result translation of narrative text from English into Indonesian by 14 participants of English third semester students. In this study, the data analyzed based on translation techniques of Molina and Albir (2002) theory. Based on data analysis, it was found that the translation techniques of narrative text entitled “Telaga Warna” from English into Indonesian done by English third semester students of University of Riau Kepulauan uses fourteen (14) translation techniques found in 1302 data while translating the text. The translation techniques were namely literal translation, calque, reduction, amplification, discursive creation, linguistic amplification, established equivalent, particularization, transposition, linguistic compression, modulation, borrowing, variation, and generalization. The most dominant technique of translation used by students was literal translation techniques
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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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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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BV Subba Rao, Et al. "Unicode-driven Deep Learning Handwritten Telugu-to-English Character Recognition and Translation System." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (November 2, 2023): 344–59. http://dx.doi.org/10.17762/ijritcc.v11i10.8497.

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Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.
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Dasari, Philemon Benison, Himanshu Verma, and G. V. M. Hariprasad. "Translation and validation of communicative quality of life in the dysarthric speaker questionnaire in telugu." Clinical Archives of Communication Disorders 6, no. 3 (December 31, 2021): 187–91. http://dx.doi.org/10.21849/cacd.2021.00591.

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Purpose: Several tools are available to assess the quality of life for speech, language, voice, and swallowing disorders in Indian languages. However, limited tools are present to assess the quality of life of dysarthria. So, the present study aims to translate and validate the Quality of Life in the Dysarthric Speaker (QoL-DyS) questionnaire into the Telugu language.Methods: A total of 30 different types of dysarthria and 30 age-matched controls participated in the present study. The QoL-DyS tool was trans-adapted in the Telugu language using a standardized procedure and administered to the participants. The internal consistency, test-retest reliability, construct validity, scale statistics, and item statistics were computed.Results: The present study revealed that the developed tool has good (i.e., 0.81) overall internal consistency and excellent (i.e., 0.92) test-retest reliability. The significant difference (i.e., <i>p</i><0.05) was found between the experimental and control group.Conclusions: From the present study, we can conclude that the Telugu QOL-DyS is a reliable tool. It is applicable in clinical practice for the self-assessment of the quality of life in dysarthric speakers. Hence, the Telugu QOL-DyS is recommended in clinical practice, and there is a need to develop QoL-DyS in other Indian languages.
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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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Dissertations / Theses on the topic "Translations from Telugu"

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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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Books on the topic "Translations from Telugu"

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editor, Suneetha Rani, ed. Vibhinna: Voices from contemporary Telugu writing. New Delhi: Sahitya Akademi, 2015.

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S, Krishna Moorthy R., and Murty N. S, eds. The palette: Short stories translated from Telugu. [Hyderabad, India]: Copies can be had from RSK Moorthy, 1997.

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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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Śrīnivās, Śiṣṭlā. The body as temple: Erotica from Telugu (2nd century B.C. to 21st century A.D.). Visakhapatnam: Drusya Kala Deepika, 2007.

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Tagore, Rabindranath. Gitanjali (song offerings): A collection of prose translations made by the author from the original Bengali. Boston, MA: Branden Publishing Company, 1992.

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Tagore, Rabindranath. Gitanjali, song offerings: A collection of prose translations made by the author from the original Bengali manuscript = Gītāñjali. New Delhi: Published by UBS Publishers' Distributors in association with Visva-Bharati, Santiniketan, 2003.

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Alladi, Uma, and Sridhar M, eds. Untouchable spring. New Delhi: Orient Blackswan, 2010.

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Tagore, Rabindranath. Song offerings (Gitanjali). London: Anvil, 2000.

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Tagore, Rabindranath. Gītāñjali-gānāmr̥tam. Hyderabad: Arun Publications, 1996.

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Tagore, Rabindranath. Gītāñjali. Paṭanā: Bihāra Magahī Akādamī, 1986.

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Book chapters on the topic "Translations from Telugu"

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Ponomareva, Anna. "The Visibility of the Translator." In Translating Russian Literature in the Global Context, 429–36. Cambridge, UK: Open Book Publishers, 2024. http://dx.doi.org/10.11647/obp.0340.26.

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This essay is auto-ethnographic. It is based on the author’s personal memories of working in Moscow during the 1980s and early 1990s at the Progress and Raduga publishing houses’ Telugu division. The article aims to address the issue of a translator’s visibility, a discussion initiated by Lawrence Venuti in The Translator’s Invisibility (1995), which heralded a new era in Translation Studies by emphasizing the importance of translators in literary creation. This essay examines translation as a collaborative activity in the context of Russian-Indian relations, where a sense of team spirit and cultural enthusiasm prevailed over state censorship and ideology. It also states that the impact of translation work, commissioned by Progress and Raduga, on Telugu readers is difficult to overestimate. Today, when there is no longer any state-sponsored book production programme between post-Soviet Russia and India, contemporary Indian readers retain great interest in books from the former USSR.
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Mogla, Radha, Chellapilla Vasantha Lakshmi, and Niladri Chatterjee. "Transliteration from English to Telugu Using Phrase-Based Machine Translation for General Domain English Words." In Lecture Notes in Electrical Engineering, 657–70. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-5936-3_61.

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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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"On the Process of Translation." In For the Lord of the Animals-Poems from The Telugu, ix—x. University of California Press, 2023. http://dx.doi.org/10.2307/jj.5233092.3.

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"When a Mountain Rapes a River, from Bhattumurti’s Telugu Vasu’s Life." In Sensitive Reading: The Pleasures of South Asian Literature in Translation, 178–92. University of California Press, 2022. http://dx.doi.org/10.1525/luminos.114.l.

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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 "Translations from 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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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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