natural language processing

Natural language processing (NLP) encompasses tasks such as coreference resolution, linking expressions referring to the same entity, knowledge graph-to-text generation from structured graphs, and KGQA transforming queries to graph queries. Large language models (LLMs) have revolutionized NLP, achieving milestones in text generation, translation, sentiment analysis, and conversation AI, driven by transformer architectures with attention mechanisms. Key models include OpenAI's GPT series for instruction-following tasks without fine-tuning, Google's BERT for contextual understanding and T5 for unified text-to-text frameworks, and Meta's RoBERTa with optimized pre-training. Challenges include hallucinations as factually inconsistent outputs, where traditional metrics like BLEU fail to assess factual correctness. Techniques like retrieval-augmented generation (RAG) by Lewis et al. (2021) address knowledge-intensive tasks, while pipelines extract roles and deontics from governance texts. Integrations with knowledge graphs enhance capabilities, as in ERNIE by Zhang et al. (2019), and neuro-symbolic approaches like CREST target NLP applications.

Natural language processing (NLP) is a computational field at the intersection of artificial intelligence, linguistics, and cognitive science, as outlined in the paper 'Perspectives for natural language processing between AI, linguistics and cognitive science' by Lenci, A. & Padó, S.. It leverages machine learning models to detect language changes driven by social and technological factors languages evolve detectably by NLP. Researchers like Haim Dubossarsky focus on NLP using mathematical methods intersecting linguistics and neuroscience Dubossarsky's NLP research, while Shayan Ray at Amazon specializes in NLP, natural language understanding, generation, and conversational AI Shayan Ray's NLP focus. Jennifer D’Souza advanced NLP in relation mining from text during her PhD and applied it to software engineering in her postdoc D’Souza's relation mining. NLP has advanced rapidly due to large datasets and computing power NLP rapid advancement factors, enabling applications like multi-task learning multi-task in NLP, prompting methods surveyed in 'Pre-train, prompt, and predict' prompting survey in NLP, personality-based LLM approaches like PsychoGAT personality approaches for LLMs, and knowledge-enhanced pre-trained models for understanding and generation per Hu et al. (2023) knowledge-enhanced NLP models. It powers question answering with broad applications QA in NLP, dialogue simulators NLP dialogue simulators, and customer segmentation via emotional profiles NLP for emotional segmentation. In knowledge graphs, NLP handles named entity recognition, relationship extraction, and entity linking NLP for KG building, with GraphRAG integrating LLM NLP capabilities GraphRAG uses NLP and pipelines employing libraries like NLTK or spaCy NLP libraries for AI. LLMs enhance NLP tasks like entity recognition LLMs improve NLP tasks, though challenges persist from language ambiguity NL ambiguity in KGs and issues like hallucinations hallucinations in NLP.