2027 9th International Conference on Natural Language Processing 2027年第九届自然语言处理国际会议

Natural Language Processing (NLP), as a significant branch of artificial intelligence, focuses on enabling computer systems to analyze, understand, and generate human language. With the rapid development of big data and deep learning technologies, NLP has achieved remarkable breakthroughs in areas such as language translation, text understanding, knowledge base construction, summarization, and human-computer interaction. Today, NLP technologies have deeply integrated into daily life, playing a vital role in fields such as entertainment, healthcare, database management, and e-governance, making it one of the core drivers of modern technology. 2027 9th International Conference on Natural Language Processing (ICNLP) will be held in Zhenjiang, China from April 16-18, 2027. ICNLP 2027 is mainly Sponsored by Jiangsu University of Science and Technology, hosted by School of Computer Science, Jiangsu University of Science and Technology, and jointly supported by many universities, industries, and academic institutions. Since its inception, the ICNLP conference series has been dedicated to advancing academic research and technological applications in NLP, providing a vital platform for researchers, students, developers, and practitioners worldwide to exchange innovative ideas and showcase cutting-edge achievements. ICNLP 2027 will cover a wide range of topics, including but not limited to natural language understanding, speech recognition, text generation, knowledge graphs, sentiment analysis, and cross-lingual information processing. The conference will feature keynote speeches by renowned experts from around the world, multiple academic discussions, and technical sharing sessions, providing participants with opportunities for in-depth exploration and collaboration. We warmly invite experts and scholars from the NLP community worldwide to join us in Zhenjiang to share their latest research achievements and collaboratively discuss the future directions of natural language processing.