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Telugu (/ ˈ t ɛ l ʊ ɡ uː /; [6] తెలుగు, Telugu pronunciation: [ˈt̪eluɡu]) is a classical Dravidian language native to the Indian states of Andhra Pradesh and Telangana, where it is also the official language.
A characteristic of Tanglish or Tamil-English code-switching is the addition of Tamil affixes to English words. [12] The sound "u" is added at the end of an English noun to create a Tamil noun form, as in "soundu" and the words "girl-u heart-u black-u" in the lyrics of "Why This Kolaveri Di".
Tamil Lexicon (Tamil: தமிழ்ப் பேரகராதி Tamiḻ Pērakarāti) is a twelve-volume dictionary of the Tamil language. Published by the University of Madras , it is said to be the most comprehensive dictionary of the Tamil language to date.
The Tamil script (தமிழ் அரிச்சுவடி Tamiḻ ariccuvaṭi [tamiɻ ˈaɾitːɕuʋaɽi]) is an abugida script that is used by Tamils and Tamil speakers in India, Sri Lanka, Malaysia, Singapore,and elsewhere to write the Tamil language. [5]
Tamil words consist of a lexical root to which one or more affixes are attached. Most Tamil affixes are suffixes. Tamil suffixes can be derivational suffixes, which either change the part of speech of the word or its meaning, or inflectional suffixes, which mark categories such as person, number, mood, tense, etc.
Dravidian languages include Tamil, Malayalam, Kannada, Telugu, and a number of other languages spoken mainly in South Asia. The list is by no means exhaustive. Some of the words can be traced to specific languages, but others have disputed or uncertain origins. Words of disputed or less certain origin are in the "Dravidian languages" list.
Tamil Nadu has the second largest economy of any state in India. [16] The state is also the most industrialised in the country. [17] [18] The state is 48.40% urbanised, accounting for around 9.26% of the urban population in the country, while the state as a whole accounted for 5.96% of India's total population in the 2011 census. [19]
Google Translate's NMT system uses a large artificial neural network capable of deep learning. [1] [2] [3] By using millions of examples, GNMT improves the quality of translation, [2] using broader context to deduce the most relevant translation. The result is then rearranged and adapted to approach grammatically based human language. [1]