Call for Papers

Foundations of Language Processing
•Data/text mining, corpus linguistics, and psycholinguistic modeling
•Basic NLP pipelines: tokenization, POS tagging, lemmatization, dependency parsing, and semantic role labeling
•Low-resource language engineering and cross-lingual adaptation

Linguistic Analysis & Understanding
•Syntax, semantics, discourse analysis, and pragmatics
•Multimodal speech recognition/synthesis (ASR/TTS) and conversational AI
•Diachronic corpora, temporal reasoning, and evolving language models

Knowledge Systems & Semantics
•Automated knowledge acquisition, ontology generation/alignment, and semantic web technologies
•Neuro-symbolic integration: combining logic-based reasoning with neural networks

Content Analysis & IR
•Topic modeling, event/anomaly detection, and sentiment/emotion analysis
•Document summarization, plagiarism detection, and authorship attribution
•Dynamic/personalized IR, adversarial retrieval, and cross-language systems

Social & Multimedia Analysis
•Personality/emotion detection in social media, misinformation tracking
•Multimodal IR (text, image, video) and virality prediction

Large Language Models (LLMs) & Transformers
•Architectures (BERT, GPT, T5, LLaMA) for NLU, generation, and few-shot learning
•Domain-specific LLMs (e.g., BioGPT, Codex) and tools like ChatGPT, DeepSeek, Claude
•Ethical challenges: bias mitigation, hallucination control, and AI-generated content detection

Generative AI & Automation
•Abstractive summarization, synthetic data generation, and conversational agents
•Multimodal LLMs (e.g., GPT-4V) for vision-language tasks

Graph & Deep Learning
•GNNs for co-occurrence graphs, knowledge graph completion, and dynamic networks
•Swarm intelligence hybridized with transformer architectures

Efficiency & Scalability
•Model compression (pruning, quantization), federated learning, and edge NLP
•Distributed training frameworks for trillion-parameter models

Human-Centric NLP
•Interactive AI: chatbots, dynamic query resolution, and personalized recommendation systems
•Explainability (XAI) and visualization of attention mechanisms

Machine Translation & Multilinguality
•Zero-shot translation, LLM-driven low-resource adaptation, and post-editing workflows

Decentralized & Collaborative Systems
•Blockchain for decentralized knowledge graphs, federated search, and privacy-preserving NLP

Ethics & Governance
•AI safety, fairness audits, and regulatory compliance (e.g., EU AI Act)
•Combatting misinformation and deepfakes in social/content platforms

AI for Science: LLMs in biomedical NLP, climate text analysis, and legal document processing
Embodied AI: Language models integrated with robotics and real-world interaction
Self-Supervised Learning: Pre-training paradigms beyond transformers