Conferences  >  Informatics  >  Natural Language Processing, Computational Linguistics

Peking University

The Tenth International Conference on Data Mining and Big Data (DMBD'2025) serves as a global platform for researchers and practitioners to exchange the latest advancements in theories, algorithms, models, and applications of data mining, big data, and artificial intelligence techniques. Data mining involves exploring large datasets to extract meaningful information, while big data encompasses vast amounts of data and information that warrant in-depth research.

Database Technology and Design, Search Engines and Data Mining,    Implementation and Applications of Artificial Intelligence

IEEE

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. 2026 8th International Conference on Natural Language Processing (ICNLP 2026) will be held in Xi'an, China from March 20 to 22, 2026. ICNLP 2026 is mainly Co-Sponsored by Xi'an University of Posts and Telecommunications and IEEE, hosted by School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, and jointly supported by many universities, industries, and academic institutions.

Email: Dunn@iact.net

1. Language Analysis and Representation: Phonetics, Phonology, and Morphology; Syntax, Semantics, Discourse, Pragmatics, Dialogue, and Lexicon; Word and Sentence Representation; word and sentence representation; POS Tagging; Parsing; Semantic Role Labelling; Word-Sense Disambiguation; Multiword Expressions; Anaphora Resolution; 2. Language Processing Models and Techniques: Complexity; Mathematical, Statistical, Machine Learning, and Deep Learning Models; Mathematical and Statistical Models; Machine Learning Models; Deep Learning Models ; 3. Language Resources and Tools: Language Resources and Corpora; Electronic Dictionaries, Terminologies, and Ontologies; Linked Data; 4. Multilingual and Cross-Lingual Processing: Multilingual NLP; Machine Translation, Translation Memory Systems, and Computer-Aided Translation Tools; Text Simplification and Readability Estimation; 5. Information Extraction and Retrieval: Knowledge Acquisition; Information Retrieval; Text Categorization; Information Extraction; Text Summarization; Terminology Extraction; Question Answering; Fact Checking;

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Speech Translation; Human Interpretation; Multilingual AI Assistant; Speech Processing; Human-Computer Interaction

Human-Computer Interaction, User Experience, Virtual Reality,    Linguistics

European Society for the Study of English

The rapid development of AI and its integration into translator training and translation practices have led to numerous changes in both academia (Bada & Schumacher, 2024; Loock, 2025) and the translation industry (Girletti & Lefer, 2024), hence a need for trainees and professionals to adapt to an ever-changing field. The necessity of mastering new AI skills, namely prompt engineering (He, 2024: 316), leads both lecturers and professionals to reinvent themselves more than ever. Although the recent AI developments have led to the use of MT for many text types that were considered, until recently, to be AI-resistant, human intervention is still needed to maintain professional quality standards, particularly for literary translation (Hansen & Esperança-Rodier, 2022: 185), creative domains (dubbing, subtitling, localisation) (e.g. Jiménez-Crespo, 2024: 18) and specialised translation (terminology management, polysemy) (Kübler et al., 2024).