M al U pama - Figurative Language Identification in M alayalam -An Experimental Study (2024)

Abstract: Figurative language, particularly in under represented languages within the Dravidian family, serves as a critical medium for conveying emotions and cultural meaning. Despite the rich literary traditions of languages such as Malayalam, Tamil, Telugu, and Kannada, there has been minimal progress in developing computational techniques to analyze figurative expressions. Historically, Malayalam was known by various names, such as Malayanma and Malabari. Similarly Kerala was known as Malanadu before adopting its current name, which metaphorically refers to the land between the Indian Ocean and the Western Ghats. In this study, we introduce the UPAMA Model(MalUpama), designed to identify Similes in Malayalam, an under-resourced Dravidian language mostly spoken in the state of southern India, Kerala. The current research focuses on detection of presence of Simile in Malayalam prose using the ‘Upama model’. This paper outlines the detection of Simile in Malayalam sentences and a detection accuracy of 94.5% is achieved by the proposed method. To the best of our knowledge this is the first work in the Malayalam language, explores computational techniques with a particular focus on applying machine learning to analyze figurative expressions which can be adopted for other Dravidian Languages too. The dataset developed for this study is made publicly available, allowing scholars to contribute and explore more on the category ‘Upama’ of Figurative Languages (‘Alankarangal’) of Malayalam language.

## introduction although dravidian languages are spoken by many, they still remain underrepresented and have not received appropriate attention in computational linguistic research. figurative language like simile can make strong emotional response and the lack of representation create a significant gap in research, especially in the dravidian languages. this limitation is particularly noticeable in malayalam, where similes, or 'upama alankara', are important rhetorical persuasion and plays a major role in expressing complex emotions in poetic expressions. the current paper presents a machine learning model, to analyze 'upama alankara'(simile) in the dravidian language malayalam. a.r. rajarajavarma(a prominent malayalam poet and scholar) in his book "bhashabhushanam" in the chapter "alankara pramanagal", states that a figurative language pro vides readers immense enjoyment, by converting a simple sentence into extra ordinary art form. additionally, it serves as cultural markers that protect language tradition in addition to enhancing poetic expressions. the phrase "my life burns and melts like a candle" conveys an emotional depth, denoting intense distress and suffering beyond a simple expression of love. thus, the study of figurative languages, especially similes, is vital for sentiment analysis because it may capture complex emotional states. ## motivation figurative language, particularly simile in underrepresented languages like malayalam in the dravidian family, plays a crucial role in expressing deep emotions and cultural significance. taraka @xcite highlighted that dravidian languages are spoken by millions of people worldwide. nlp in dravidian languages face many challenges and at the same time give exciting research opportunities in the field of sentiment analysis, stress detection, machine translation etc. the main objective of this research is to create an 'upama model-malupama' to identify the figurative language simile in sentences and thus contribute to the language technology in resource-poor dravidian languages like malayalam. in malayalam poetry and prose, upama alankaram (simile) enriches the sentence's aesthetic beauty by crafting vivid imagery. this helps readers or listeners to clearly perceive the comparisons between different objects or ideas. the creation of the 'upama model-malupama' offers a method for detecting similes in malayalam. it sets the groundwork for computational studies across various dravidian languages. ## literature review simile detection in natural language processing (nlp) is a special and developing area of research. we conducted an extensive search for literature focusing on dravidian languages. however, the available studies in this domain are found to be quite limited. so, we expanded our research area to encompass english, cross-lingual languages and indian languages thus enabling a deeper understanding of the current research initiatives in simile identification within natural language processing. this expansion provided deeper insights into the methodologies and progress made in this field. advances in deep learning and machine learning techniques have improved simile detection systems' efficiency and precision in a variety of languages. the literature review is organized into three main categories. ## dataset preparation in order to effectively train any machine learning model, a large amount of data is essential. the dataset preparation for 'upama model-malupama' required several key steps to ensure that the data is appropriate for both analysis and model training. below is a summary of the dataset preparation process as carried out in this research work. malayalam boasts a wealth of literary and general texts, offering a broad selection of material for linguistic study. we have tried to extract lines from malayalam news portals and various social media platforms. however, similes are scarce in sentences, appearing in roughly two out of every hundred sentences. despite the existence of many books, newspapers, magazines, and online resources, there is still a significant lack of a comprehensive text corpus, particularly in the area of figurative language. the development of nlp tools for malayalam and computational linguistics research are hampered by this limitation. to overcome this limitation, we developed a specialized malayalam text corpus centered around similes. this corpus is designed to aid computational linguists, nlp researchers, and scholars by providing vital resources for linguistic feature analysis, research, and information retrieval tasks. understanding the significance of such a corpus, we curated a collection of malayalam simile sentences, which plays an essential role in simile model creation. the process of creating this dataset was carefully planned and executed, using large language model tools such as chatgpt, google gemini, and claude ai. chatgpt, a dialogue generation tool based on transformer architecture, was used for this task, having been trained on extensive conversational data to generate human-like text. similes are heavily used in various forms in normal conversations in malayalam, the machine generated sentences can be easily anlayzed for correctness. with assistance from selected volunteers, we compiled a dataset of 1,000 simile sentences and 1,000 non-simile sentences in malayalam. figure 1 shows the block diagram of the method we used for the data set creation. ## methodology the development of simile detection model-'malupama' in malayalam focuses on detecting and analyzing the figurative language upama (simile) using machine learning techniques. the flow diagram of malupama is displayed in figure 5 . figure 5: flow diagram of the upama model ## experimental analysis we ensured that all lines that passed the validation steps during preprocessing are ready for training and prediction. two thousand lines of malayalam text were created for this purpose and used for training and testing. the training phase used the presence/absence of "പപാലെ" as the feature (x) and the corresponding label (y) as the target variable. hyper-parameter 'rbf' random state in svm is set empirically as 0.42. the model is trained using 80% of the data and its performance evaluated using the test set, the remaining data in the dataset. the model shows the accuracy of 94.5%, and a precision of 98% as shown in