  This paper delves into the text processing aspects of Language Computing,
which enables computers to understand, interpret, and generate human language.
Focusing on tasks such as speech recognition, machine translation, sentiment
analysis, text summarization, and language modelling, language computing
integrates disciplines including linguistics, computer science, and cognitive
psychology to create meaningful human-computer interactions. Recent
advancements in deep learning have made computers more accessible and capable
of independent learning and adaptation. In examining the landscape of language
computing, the paper emphasises foundational work like encoding, where Tamil
transitioned from ASCII to Unicode, enhancing digital communication. It
discusses the development of computational resources, including raw data,
dictionaries, glossaries, annotated data, and computational grammars, necessary
for effective language processing. The challenges of linguistic annotation, the
creation of treebanks, and the training of large language models are also
covered, emphasising the need for high-quality, annotated data and advanced
language models. The paper underscores the importance of building practical
applications for languages like Tamil to address everyday communication needs,
highlighting gaps in current technology. It calls for increased research
collaboration, digitization of historical texts, and fostering digital usage to
ensure the comprehensive development of Tamil language processing, ultimately
enhancing global communication and access to digital services.
