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.

翻译：本文深入探讨语言计算的文本处理方面，该领域使计算机能够理解、解释和生成人类语言。聚焦于语音识别、机器翻译、情感分析、文本摘要和语言建模等任务，语言计算整合了语言学、计算机科学和认知心理学等学科，以创建有意义的人机交互。深度学习的最新进展使计算机更易于使用，并具备独立学习和适应的能力。在审视语言计算的发展现状时，本文强调了基础性工作，如编码——泰米尔语从ASCII向Unicode的过渡，从而增强了数字通信能力。文章讨论了计算资源的开发，包括有效语言处理所需的原始数据、词典、术语表、标注数据和计算语法。同时涵盖了语言标注的挑战、树库的构建以及大语言模型的训练，强调了高质量标注数据和先进语言模型的必要性。本文着重指出为泰米尔语等语言构建实际应用以满足日常交流需求的重要性，并揭示了当前技术存在的不足。文章呼吁加强研究合作、推进历史文献数字化并促进数字应用，以确保泰米尔语语言处理的全面发展，最终提升全球交流能力与数字服务的可及性。