Dr.S.Jothilakshmi
Assistant. Professor of Computer Science
The Standard Fireworks Rajaratnam college for Women
Sivakasi.
Abstract
Artificial Intelligence (AI) tools are revolutionizing the way we interact with and utilize languages around the world. In the case of the Tamil language, the integration of AI technologies opens up exciting possibilities for natural language processing, machine translation, speech recognition, and various other applications tailored to the unique characteristics of Tamil language. In this paper some of the AI tools are discussed for the development and enhancement of the Tamil language.
Introduction
Artificial Intelligence is a subfield of computer science. It aims to build machines that are able to carry out tasks that would normally need human intelligence. The goal of AI is to develop machines and software that can mimic cognitive functions such as learning, problem-solving, perception, and language understanding. Artificial Intelligence (AI) is a massive field with many different approaches, methods, and uses. It has greatly advanced many different industries.
Artificial intelligence (AI) is a multidisciplinary field that combines computer science, mathematics, and cognitive science. Its many uses have the capacity to transform entire industries, improve efficiency, and solve challenging issues. As AI continues to advance, ethical considerations and responsible development practices are crucial to ensure its positive impact on society.
Tamil language growth and enrichment could be greatly aided by artificial intelligence (AI).As technology continues to advance, integrating AI into Tamil language applications can open up new possibilities for communication, education, and cultural preservation. Here are several ways AI can contribute to Tamil language development
While implementing AI in Tamil language development, it is crucial to consider linguistic diversity, cultural sensitivities, and ethical considerations. Collaborations between AI developers, linguists, and Tamil language experts can ensure the responsible and culturally relevant deployment of AI technologies in the Tamil language space.
Natural Language Processing Tools for Tamil Language
AI-driven NLP tools can be tailored for Tamil, enabling machines to understand, interpret, and generate human-like text. This includes tasks such as sentiment analysis, named entity recognition, and language translation. Developing robust NLP models in Tamil enhances the language’s digital presence.
NLTK(Natural Language Toolkit
NLTK is a powerful Python library for working with human language data. While it may not have specific tools for Tamil, You can use NLTK for basic text processing tasks in Tamil.we can use its general NLP functionalities for tasks like tokenization, stemming, and part-of-speech tagging.
Spacy
Spacy is another popular NLP library for Python. While its models may not be as comprehensive for Tamil as for some other languages, it can still be a valuable tool for basic NLP tasks.
Language Models
Building machine learning models for NLP requires a large and diverse dataset. Look for Tamil language corpora for tasks like sentiment analysis, text classification, and language modelling.Train or fine-tune language models on Tamil language data. You can use frameworks like Hugging Face Transformers or TensorFlow to work with pre-trained models like BERT, GPT, or others for Tamil.
Multilingual BERT(Bidirectional Encoder Represenations fromTransformers)
BERT is a pre-trained language model that can be fine-tuned for various NLP tasks. While not specifically designed for Tamil, it has a multilingual version that can be adapted for Tamil language processing.
Tamil BERT
If available, a language model specifically pre-trained for Tamil, such as Tamil BERT, would be beneficial. Check for any community or research efforts focused on developing language models for Tamil.
Mahine Translation
Develop machine translation models to translate text between Tamil and other languages. Neural machine translation models like Transformer models can be used for this purpose.
Google Translate
Google Translate supports translation to and from Tamil. Google Translate is a machine translation service provided by Google that utilizes neural machine translation technology to automatically translate text between different languages.
Open NMT
Open NMT is an open-source neural machine translation framework. You can train models specifically for English to Tamil or vice versa translation using parallel corpora.
Speech Recognition
Speech recognition AI tools for Tamil convert spoken words into text, enabling hands-free communication and accessibility features. This technology has applications in voice-activated systems, transcription services, and voice-controlled interfaces.
Google’s Speech-to-Text API
Google’s Speech-to-Text API supports multiple languages, including Tamil. It can be used to transcribe spoken Tamil into text.
Openaichat
This too is used t0 Elevate your Tamil communication: From transcribing voice to typing text flawlessly, find out how new tailor-made tools are bringing Tamil language.
Text to -Speech
TTS tools for Tamil convert written text into spoken words with natural intonation and pronunciation. This is valuable for creating audio content, enhancing accessibility for visually impaired individuals, and improving user experiences in various applications.
GOOGLE Cloud Text-to-Speech API
This API can convert text into natural-sounding speech. It supports multiple languages, including Tamil.
Dubverse
Dubverse is a Tamil text-to-speech app that uses advanced AI technology to generate high-quality voice output. It has a user-friendly interface that allows users to input any Tamil text and convert it into an audio file.
Narakeet
Narakeet Tamil text to speech online Voices make it easy to produce Tamil voiceovers, audio materials and videos. Natakeet has the best text to speech tamil voices, based on latest AI technology that produces realistic, natural Tamil text to speech online.
Murf AI
Tamil Text to Speech : Create Lifelike Tamil Accent AI Voices for Free. Craft Natural Tamil Text to Audio (TTS) with Murf’s Tamil Voice Generator.
Resemble AI
It is a voice generator It offers text-to-speech, speech-to-speech, neural audio editing, language dubbing, emotions, real-time voice cloning, localize, and Resemble Fill capabilities. It also provides a flexible API and integrations with popular tools, enabling developers to rapidly build production-ready integrations
Chatbots and Conversational Agents
AI-powered chatbots and conversational agents can be developed to interact with users in Tamil. These systems can be designed for customer support, information retrieval, or other conversational applications.
POONGKUZHALI-An Intelligent Tamil Chatterbot
Poongkuzhali is a Chatterbot that simulates human conversation through Artificial Intelligence-a program to chat with the system in Tamil, to generate an appropriate response, based on the context of the input.
Multilingual chatbot
i.e. a bot that can converse with users in multiple languages, can be a tremendous asset to any organization. This particularly holds true in a highly linguistically diverse country like India. The digital revolution in India has exponentially broadened the Internet user base in the country to include large numbers of non-English speakers, who vastly outnumber English language speakers in the country
Named Entity Recognition (NER)
AI models can be trained for identifying and classifying entities such as names, locations, and organizations in Tamil text. This is useful for applications like information extraction and content categorization.
Named entity recognition (NER) helps to identify the key elements in a text, like names of people, places, brands, monetary values, and more. Extracting the main entities in a text helps sort unstructured data and detect important information, which is crucial ito deal with large datasets.
- Document Classification
AI can be applied to automatically categorize or classify documents in Tamil. This can be useful for organizing large sets of documents, such as news articles or research papers.
1Document AI
Structure document data that you can store, analyze, search, and use to automate processes. Document AI extracts data from, classifies, and splits documents through a suite of pretrained models or through Workbench custom models. Finally, it uses Warehouse to search and store documents.
10.Sentiment Analysis
AI models can be trained to analyze the sentiment expressed in Tamil text. This can be applied to social media monitoring, customer feedback analysis, and other applications where understanding sentiment is important.
- Language Generation
AI can be used to generate human-like text in Tamil. This can be applied to content creation, creative writing, or even for generating suggestions in various applications.
Natural language generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narratives from a data set. NLG is related to human-to-machine and machine-to-human interaction, including computational linguistics, natural language processing (NLP) and natural language understanding (NLU).
- Educational Tools
AI tools can be used to develop educational applications for learning Tamil. This can include interactive language learning platforms, automated tutoring systems, or tools for practicing language skills.
CONCLUSION
In conclusion, the development of AI tools for the Tamil language is a promising and evolving field that holds significant potential for enhancing various aspects of language processing, understanding, and communication. The ongoing collaboration between AI developers, linguists, and the Tamil-speaking community will play a pivotal role in shaping the future of AI tools for the Tamil language. As technology continues to evolve, the development of specialized tools that cater to the unique linguistic and cultural aspects of Tamil will contribute to a more inclusive and effective AI ecosystem.
References
Abinaya, N., John, N., Ganesh, B. H., Kumar, A. M.,and Soman, K. (2014). “Amrita cen@ fire-2014: Named entity recognition for Indian languages usingrich features.” In: Proceedings of the Forum forInformation Retrieval Evaluation, pages 103-111ACM
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Arulmozhi P, Sobha L and Kumara Shanmugam B.(2004). “Parts of Speech Tagger for Tamil”,Symposium on Indian Morphology, Phonology &Language Engineering, March 19-21, IITKharagpur. : 55-57