Faculty of Computing

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Revolutionizing Tamil Language Analysis: A Natural Language Processing Model Development Approach
(IEEE, 2024-07-25) Ravichandira, G; Sivabaskaran, V; Uthayakumar, T; Vyravanathan, S; Krishara, J; Rajendran, K
This study proposes a web-based platform utilizing Natural Language Processing (NLP) techniques to identify and rectify spelling and grammar errors in Tamil, a language with intricate nuances. Users can input Tamil text, which undergoes automated scrutiny for linguistic inaccuracies. Additionally, the research delves into contextual text summarization and real-time transcription of spoken Tamil. The overarching aim is to devise a holistic solution amalgamating various components to facilitate the detection and rectification of Tamil spelling and grammatical errors. The envisioned subgoals encompass a spell-checking tool capable of identifying misspelled words and suggesting appropriate replacements based on context, a grammar correction feature adept at identifying and rectifying grammatical inaccuracies while accommodating the unique grammatical structures of Tamil, a summarization component adept at condensing paragraphs while retaining core concepts, and a transcription feature enabling the real-time conversion of spoken Tamil into accurate text. By addressing the complexities of the Tamil language, this research endeavor seeks to contribute to the expansion of language processing tools. The ultimate objective is to empower users with the means to detect and rectify errors while enhancing their proficiency in spoken Tamil. This synthesis of components represents a significant stride towards the development of a comprehensive web-based platform for identifying and rectifying Tamil spelling and grammar errors.
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Deep Learning for Code-Mixed Text Mining in Social Media: A Brief Review
(Springer, Cham, 2022-09-19) Panchendraraja, R; Saxena, A
The advent of social media in day-to-day life has made communications between people more often and easier than ever before. Analyzing the content in social media has opened up a massive amount of research and commercial opportunities. However, the content in social media is noisy and multi-lingual, which postures computational challenges ahead. Especially, the non-native English speakers and writers tend to mix their native language with English while generating social media content. Thus it requires a comprehensive prepossessing of text, including the identification of language for many language processing applications. In the area of language processing, deep learning has shown to be very successful, and the latest research works have witnessed the adoption of deep learning solutions to cater to the challenges in analyzing code-mixed text. Here, we highlight a comprehensive study of deep learning techniques used for analyzing the code-mix text of social media to understand the state-of-the-art and existing research challenges. We will discuss several applications of code-mixed text analysis and future directions.
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EasyChat: A Chat Application for Deaf/Dumb People to Communicate with the General Community
(Springer, Cham, 2022-07-07) Sriyaratna, D; Samararathne, W. A. H. K.; Gurusinghe, P. M.; Gunathilake, M. D. S. S.; Wijenayake, W. W. G. P. A.
Sign Language is closely associated with the deaf and dumb community to communicate with each other. However, not everyone understands sign language or verbal languages, so these communities need proper ways to communicate online. Therefore, this paper presents EasyChat, a sign language chat application that can translate three main sign languages into Simple English text as well as Simple English text into sign language, which would benefit for deaf/dumb community to express their ideas with the general community by simply capturing their British Sign Language (BSL) or Makaton gestures/symbols or lip movements. These steps are handled by four components. The first component, Convert BSL into Simple English, and the second component, handles Lip Reading conversion. The Makaton gesture and symbol conversion component produces a simple English text-formatted output for identified Makaton hand signs. Finally, the Text/voice to Sign Converter works on converting entered English text back into the sign language-based images. By using these components, EasyChat can detect relevant gestures and lip movement inputs with superior accuracy and translate. This can lead to more effective and efficient online communication between the community of deaf/dumb individuals and the general public.
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A novel modular neuro-fuzzy controller driven by natural language commands
(IEEE, 2001-07-27) Watanabe, K; Pulasinghe, K; Kiguchi, K; Izumi, K
A method of interpreting imprecise natural language commands to machine understandable manner is presented in this paper. The proposed method tries to ease the process of man-machine interaction by combining the theoretical understanding of artificial neural networks and fuzzy logic. Both fields are very popular to mimic the human behavior in different research areas in artificial intelligence. The proposed system tries to understand the natural language command rather than mere recognition. The distinctive features of the artificial neural networks in pattern recognition and classification and the abilities of manipulating imprecise data by fuzzy systems are merged to recognize the machine sensitive words in the natural language command and then to interpret them to machine in machine identifiable manner. Modularity of the design tries to break up the complete task into manageable parts where the presence of individual part is vital to bridge the so-called man-machine gap.

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