Analysis of Uvama Urubugal in T amil Sangam Literatures (2021)

Abstract: Uvama urubugal in Tamil are used to explain a particular context by citing another equivalent context. This is referred to as “Uvamaiyani” in Tamil Grammar rules as stated in Tholkappiam. The is called as simile in English. Similes bring out many beautiful poetic contexts. Automatic extraction of such similes can help to build better Natural Language Generation applications such as, story generation systems and lyric suggestion systems. This paper attempts to automatically extract the uvama urubugal from Tamil Sangam Literatures. Natrinai and Mullai Pattu have been used for the analysis. There are 12 uvama urupugal in Tamil as per Nanool and this paper has attempted to analyze the usage of these 12 uvama urubugal in Sangam Literatures and compares their usage distribution in the Tamil Film songs data set comprising of 4215 songs. It was found that only two uvama urubugal were used in the current-day Tamil Film songs. This comparison was done to reveal the diminishing usage of these beautiful uvama urubugal by the current generation and the urge to use them again.

## introduction similes bring out different perception to a context and they add beauty to a text. in tamil language, similes are termed as uvamai and follows the grammar rules called, "uvamayani". the "uvama urubugal" are those words that are used to frame the simile. there are 12 uvama urubugal in tamil. the tamil uvama urubugal and their respective english transliteration is given in tamil language is one of the ancient languages in the world and the epigraphic attestations of tamil have been written from the 6th century bc @xcite (jesudasan, 2019) . since then, tamil language has transformed a lot in terms of its written form and speech form. these transformations have also reflected the beauty of the language followed during every time period through the word usages. this paper focuses on the language usage followed in sangam literature era that spanned from 600 bce to 300 ce @xcite . this paper tries to understand the simile usage in the two sangam literatures namely, mullai pāṭu and natrinai and has compared with 4215 tamil film songs dataset @xcite . the aim of this experiment is to increase the usage of these similes by the current tamil generation. also, this paper lays a foundation to explore more on applications such as, automatic lyric generation system and automatic story generation system that can be built on top of an automatic simile detection system.the word, "sangam literature" used in the rest of the paper denotes only natrinai and mullaipāṭu. the contribution of this paper is two-fold. 1. analysing the usage distribution of twelve types of similes in two sangam literatures namely, natrinai and mullai pāṭu. three types of data sets have been used for the analysis of uvama urubugal namely, 400 natrinai songs, 103 lines of mullai pāṭu and 4215 tamil film songs(siva subramanian, 2020). https://www.kaggle.com/sivaskvs/tamilsongs-lyrics-dataset. the rest of the paper is organized as follows. section 2 describes the literature survey, section 3 describes the experimental set up that extracts and analyses the uvama urubugal and section 4 describes the conclusion and future work. ## literature survey this section focuses on three dimensions of the state-of -the art works namely, existing works on simile, works on sangam literature, and works on lyric generation. since the paper is about type of similes, the existing works that have focused on simile analysis in other languages are discussed. furthermore, the work that are done pertinent to sangam literature are also discussed. the automatic simile detection can lead to building lyric generation system and hence the works done in tamil lyric generation system are discussed. @xcite have attempted to classify the similes using convolution neural networks (cnn) and machine learning algorithms for english language. @xcite have attempted to generate similes using pretrained model bart which is a sequence-to-sequence models for english language. the paper currently identifies the types of simile (uvama urubugal) in sangam literature. deep learning models must be used as the extension of this work when the similes and their equivalent explanations are automatically extracted using the types of uvama urubugal. this kind of a deep learning-based classification can be used as the foundation for building automatic lyric generation system. ## conclusion and future work similes capture the different yet beautiful dimensions of the language. uvama urubu in tamil is used to express the similes. tamil language has seen many transformations in terms of it's word usage both in text and speech forms since 2nd century bc. there are 12 uvama urupugal in tamil language and currently pōla and māṉ a are only used. as this is an initial experiment it is tested only with natrinai and mullai pāṭu. it has to be tested with rest of the sangam songs to get a wider view. this work has many directions in which it can be extended. the experiment discussed can be viewed as a multi-classification problem and can be implemented using machine learning and deep learning algorithms to automatically classify the similes and their equivalent contexts. the contextual features must be identified for each uvama urubu and the model needs to be trained with many tamil literature texts. the extraction of these similes and their contexts can throw a lot of light in creative usage of tamil language mainly in natural language generation-based applications such as lyric generation and story generation. approach for simile generation. arxiv preprint arxiv:2009.08942. dennis s. jesudasan. 2019. keezhadi excavations: sangam era older than previously thought, finds study. the hindu. pd manjusha and c raseek. 2018. convolutional neural network based simile classification system. in 2018 international conference on emerging trends and innovations in engineering and technological research (icetietr), pages 1-5. ieee. upinder singh. 2008. a history of ancient and early medieval india: from the stone age to the 12th century. pearson education india. franklin southworth. 2004. linguistic archaeology of south asia. routledge. rajeswari sridhar, subasree venkatsubhramaniyen, and s subha rashmi. 2018. automatic singable tamil lyric generation for a situation and tune based on causal effect. in 2018 international conference on advances in computing, communications and informatics (icacci), pages 1152-1159. ieee. cn subalalitha. 2019. information extraction framework for kurunthogai. sādhanā, 44(7):1-6. jiali zeng, linfeng song, jinsong su, jun xie, wei song, and jiebo luo. 2020. neural simile recognition with cyclic multitask learning and local attention. in proceedings of the aaai conference on artificial intelligence, volume 34, pages 9515-9522.