Relevant bibliographies by topics / Telugu and Tamil

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Telugu and Tamil'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Telugu and Tamil"

1

Prabhakaran, Varijakshi. "Tamil lexical borrowings in South African Telugu." South African Journal of Linguistics 12, no. 1 (February 1994): 26–31. http://dx.doi.org/10.1080/10118063.1994.9724343.

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2

Sulochana, N. "தெலுங்கு உறவுமுறைச் சொற்கள்." Shanlax International Journal of Tamil Research 5, no. 1 (July 1, 2020): 84–89. http://dx.doi.org/10.34293/tamil.v5i1.3410.

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Kinship is a fundamental feature, the most universal and basic of all human relationships, and it is based on the ties of blood, marriage, or adoption. Kinship terminologies consist of the terms used to refer to culturally recognized kinship relations between people. Research in the anthropology field has shown that kin terminologies have a complex combinatorial structure, and it varies systematically across all the cultures and traditions. The study examines the kinship terms in the Telugu language spoken in the southern part of Tamil Nadu. The main intention of the article is to bring up the importance of having a dictionary for regional kinship terms. The study focuses on the comparison between Telugu and Tamil kinship terms. It attempts to explore the pronunciation of how it varies from Tamil and Telugu languages.
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Emeneau, M. B. "The Palatalizing Rule in Tamil-Malayalam and Telugu." Journal of the American Oriental Society 115, no. 3 (July 1995): 401. http://dx.doi.org/10.2307/606217.

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Prabhakaran, Varijakshi. "Tamil influence on South African Telugu — phonological adjustments." South African Journal of Linguistics 12, no. 2 (May 1994): 67–74. http://dx.doi.org/10.1080/10118063.1994.9724350.

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Vijaya, M., S. Kanthimathi, C. R. Srikumari, P. Govinda Reddy, P. P. Majumder, and A. Ramesh. "A Study on Telugu – Speaking Immigrants of Tamil Nadu, South India." International Journal of Human Genetics 7, no. 4 (December 2007): 303–6. http://dx.doi.org/10.1080/09723757.2007.11886010.

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6

Bharti, Santosh Kumar, Reddy Naidu, and Korra Sathya Babu. "Hyperbolic Feature-based Sarcasm Detection in Telugu Conversation Sentences." Journal of Intelligent Systems 30, no. 1 (July 3, 2020): 73–89. http://dx.doi.org/10.1515/jisys-2018-0475.

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AbstractRecognition of sarcastic statements has been a challenge in the process of sentiment analysis. A sarcastic sentence contains only positive words conveying a negative sentiment. Therefore, it is tough for any automated machine to identify the exact sentiment of the text in the presence of sarcasm. The existing systems for sarcastic sentiment detection are limited to the text scripted in English. Nowadays, researchers have shown greater interest in low resourced languages such as Hindi, Telugu, Tamil, Arabic, Chinese, Dutch, Indonesian, etc. To analyse these low resource languages, the biggest challenge is the lack of available resources, especially in the context of Indian languages. Indian languages are very rich in morphology which pose a greater challenge for the automated machines. Telugu is one of the most popular languages after Hindi among Indian languages. In this article, we have collected and annotated a corpus of Telugu conversation sentences in the form of a question followed by a reply for sarcasm detection. Further, a set of algorithms are proposed for the analysis of sarcasm in the corpus of Telugu conversation sentences. The proposed algorithms are based on hyperbolic features namely, Interjection, Intensifier, Question mark and Exclamation symbol. The achieved accuracy is 94%.
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7

Daniels, Sarah I., John C. Chambers, Sylvia S. Sanchez, Michele A. La Merrill, Alan E. Hubbard, Anthony Macherone, Matthew McMullin, et al. "Elevated Levels of Organochlorine Pesticides in South Asian Immigrants Are Associated With an Increased Risk of Diabetes." Journal of the Endocrine Society 2, no. 8 (May 22, 2018): 832–41. http://dx.doi.org/10.1210/js.2017-00480.

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Abstract Objective Rates of diabetes mellitus are higher in South Asians than in other populations and persist after migration. One unexplored cause may be higher exposure to persistent organic pollutants associated with diabetes in other populations. We compared organochlorine (OC) pesticide concentrations in South Asian immigrants and European whites to determine whether the disease was positively associated with OC pesticides in South Asians. Research Design and Methods South Asians of Tamil or Telugu descent (n = 120) and European whites (n = 72) were recruited into the London Life Sciences Population Study cohort. Blood samples as well as biometric, clinical, and survey data were collected. Plasma levels of p,p′-dichlorodiphenyldichloroethylene (DDE), p,p′- dichlorodiphenyltrichloroethane, β-hexachlorohexane (HCH), and polychlorinated biphenyl-118 were analyzed by gas chromatography-mass spectrometry. South Asian cases and controls were categorized by binary exposure (above vs below the 50th percentile) to perform logistic regression. Results Tamils had approximately threefold to ninefold higher levels of OC pesticides, and Telugus had ninefold to 30-fold higher levels compared with European whites. The odds of exposure to p,p′-DDE above the 50th percentile was significantly greater in South Asian diabetes cases than in controls (OR: 7.00; 95% CI: 2.22, 22.06). The odds of exposure to β-HCH above the 50th percentile was significantly greater in the Tamil cases than in controls (OR: 9.35; 95% CI: 2.43, 35.97). Conclusions South Asian immigrants have a higher body burden of OC pesticides than European whites. Diabetes mellitus is associated with higher p,p′-DDE and β-HCH concentrations in this population. Additional longitudinal studies of South Asian populations should be performed.
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8

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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9

Kumar K., Vimal, and Divakar Yadav. "Word Sense Based Hindi-Tamil Statistical Machine Translation." International Journal of Intelligent Information Technologies 14, no. 1 (January 2018): 17–27. http://dx.doi.org/10.4018/ijiit.2018010102.

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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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10

A, Sridevi. "Olaichuvadi Or Aaivu." International Research Journal of Tamil 3, S-1 (June 21, 2021): 213–17. http://dx.doi.org/10.34256/irjt21s134.

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The sanga noolgal andru Olaichuvadi were sleeping on the yon. In Tamil research, you began to engage in U. V. Swaminatha Iyer. After the disappearance of the disappearance, his home had received 500 running walls. Among them have received ancient texts, including Eight Anthologies, Ten Idylls, Pathinen Keelkanaku, Civaka Cintamani and Konguvel Makkathai. On that time, the account is writing. Talking is the name of the original (trace) written plates. At that time, the king of the king was written by the writer of the writer and their leader of their leader. There is a man who calls a message that has come to say that the wedding messages are Magnificent. Round-shaped characters are written only in writing writing. Tamil, Kirtham, Malayalam, Hangathari, Nandinagari, Kannada, Telugu, Oriya, Sinhala, Burmese and Thai. Initially does not point in the Tamil writings Later in 1850, you can see the point in the traces. I want to ask for today's generations to try to study or protect our wealth in the destruction of my inspection and endorses.
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Dissertations / Theses on the topic "Telugu and Tamil"

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Seastrand, Anna Lise. "Praise, Politics, and Language: South Indian Murals, 1500-1800." Thesis, 2013. https://doi.org/10.7916/D8ZS2WJB.

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This study of mural painting in southern India aims to change the received narrative of painting in South Asia not only by bringing to light a body of work previously understudied and in many cases undocumented, but by showing how that corpus contributes vitally to the study of South Indian art and history. At the broadest level, this dissertation reworks our understanding of a critical moment in South Asian history that has until recently been seen as a period of decadence, setting the stage for the rise of colonial power in South Asia. Militating against the notion of decline, I demonstrate the artistic, social, and political dynamism of this period by documenting and analyzing the visual and inscriptional content of temple and palace murals donated by merchants, monastics, and political elites. The dissertation consists of two parts: documentation and formal analysis, and semantic and historical analysis. Documentation and formal analysis of these murals, which decorate the walls and ceilings of temples and palaces, are foundational for further art historical study. I establish a rubric for style and date based on figural typology, narrative structure, and the way in which text is incorporated into the murals. I clarify the kinds of narrative structures employed by the artists, and trace how these change over time. Finally, I identify the three most prevalent genres of painting: narrative, figural (as portraits and icons), and topographic. One of the outstanding features of these murals, which no previous scholarship has seriously considered, is that script is a major compositional and semantic element of the murals. By the eighteenth century, narrative inscriptions in the Tamil and Telugu languages, whose scripts are visually distinct, consistently framed narrative paintings. For all of the major sites considered in this dissertation, I have transcribed and translated these inscriptions. Establishing a rubric for analysis of the pictorial imagery alongside translations of the text integrated into the murals facilitates my analysis of the function and iconicity of script, and application of the content of the inscriptions to interpretation of the paintings. My approach to text, which considers inscriptions to be both semantically and visually meaningful, is woven into a framework of analysis that includes ritual context, patronage, and viewing practices. In this way, the dissertation builds an historical account of an understudied period, brings to light a new archive for the study of art in South Asia, and develops a new methodology for understanding Nayaka-period painting. Chapters Three, Four, and Five each elaborate on one of the major genres identified in Chapter Two: narrative, figural, and topographic painting. My study of narrative focuses on the most popular genre of text produced at this time, talapuranam (Skt. sthalapurana), as well as hagiographies of teachers and saints (guruparampara). Turning to figural depiction, I take up the subject of portraiture. My study provides new evidence of the active patronage by merchants, religious and political elites through documentation and analysis of previously unrecorded donor inscriptions and donor portraits. Under the rubric of topographic painting I analyze the representation of sacred sites joined together to create entire sacred landscapes mapped onto the walls and ceilings of the temples. Such images are closely connected to devotional (bhakti) literature that describes and praises these places and spaces. The final chapter of the dissertation proposes new ways of understanding how the images were perceived and activated by their contemporary audiences. I argue that the kinesthetic experience of the paintings is central to their concept, design, and function.
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Books on the topic "Telugu and Tamil"

1

Saravanapavan, Kanagasabapathy. Deutsch - Tamil wörterbuch =: Jērman̲ - Tamil̲ akarāti. Kleve, West Germany: Trinko Publishers, 1992.

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2

Rādhākr̥ṣṇaśarma, Callā. Telugu Tamiḷa kavitvālu, jātīyavādaṃ. Haidarābādu: Yuvabhārati, 1994.

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3

Paḷanivēlu, Nirmalā. Telugu-Tamiḷa jānapada gēyamulu, tulanātmaka pariśīlanamu. Madras: Institute of South Indian Studies, 1992.

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4

Ar̲avēntan̲, Irā. Camūka varalār̲r̲iyal nōkkil Tamil̲um Teluṅkum. Nākarkōvil: Kālaccuvaṭu Patippakam, 2008.

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Ar̲avēntan̲, Irā. Camūka varalār̲r̲iyal nōkkil Tamil̲um Teluṅkum. Nākarkōvil: Kālaccuvaṭu Patippakam, 2008.

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Ar̲avēntan̲, Irā. Camūka varalār̲r̲iyal nōkkil Tamil̲um Teluṅkum. Nākarkōvil: Kālaccuvaṭu Patippakam, 2008.

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Bālakr̥ṣṇāreḍḍi, Eḍla. Viṣṇubhakti tatparuḍu-Vipranārāyaṇa, Telugu sāhityaṃlō tatsambandha caritra: Pariśōdhanā vyāsamu. Śrīśailaṃ Prājakṭu, Karnūlu Jillā: Caitanya Pracuraṇalu, 1985.

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Rājammā, Ṭi. Āntira pal̲amol̲ikaḷum Tamil̲ mutumol̲ikaḷum. Cen̲n̲ai: Ār. Es. Pi. Papḷikēṣan̲s, 2014.

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9

The wisdom of poets: Studies in Tamil, Telugu, and Sanskrit. New Delhi: Oxford University Press, 2001.

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10

Lakṣmīnārāyaṇa, Gaṅgiśeṭṭi. Common man's multilingual dictionary: Tulu-Kannada-Telugu-Tamil-Malayalam-English. Edited by Dravidian University. Kuppam: Dravidian University, 2011.

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Book chapters on the topic "Telugu and Tamil"

1

Bai, B. Lakshmi. "Development of Negation in Tamil-Telugu Bilingual Children." In The Fergusonian Impact, 153–70. De Gruyter, 1986. http://dx.doi.org/10.1515/9783110873641-013.

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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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Kusuma, Krishna Sankar. "South Indian Cinema." In Handbook of Research on Social and Cultural Dynamics in Indian Cinema, 303–13. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-3511-0.ch025.

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Cinema scholars often refer to Hindi cinema as Indian cinema. India has diverse languages, cultures, and a long history of Cinema of its own. Regional cinema in numbers, as well as quality, competes with any cinema in the world. The study is an attempt to present the case of five film industries in the southern part of India, which includes Tamil, Telugu, Malayalam, Kannada, and Tulu language films. The southern film industries is theorized as 'South Indian cinema' as they share similar features, yet each one of them is unique. South Indian cinema has often been looked down upon as it is cheap and vulgar. The research also explores the gender dimensions in both the industry as well as on-screen presentation. This chapter aims to provide a theoretical and philosophical interpretation of South Indian cinema.
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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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Conference papers on the topic "Telugu and Tamil"

1

Prasanth, L., V. Babu, R. Sharma, G. V. Rao, and Dinesh M. "Elastic Matching of Online Handwritten Tamil and Telugu Scripts Using Local Features." In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) Vol 2. IEEE, 2007. http://dx.doi.org/10.1109/icdar.2007.4377071.

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Renjith, S., and K. G. Manju. "Speech based emotion recognition in Tamil and Telugu using LPCC and hurst parameters — A comparitive study using KNN and ANN classifiers." In 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT). IEEE, 2017. http://dx.doi.org/10.1109/iccpct.2017.8074220.

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