Natural Language Processing (NLP) is the subfield of AI focused on enabling computers to understand, interpret, generate, and manipulate human language. Core tasks include text classification, named entity recognition, machine translation, sentiment analysis, question answering, and language generation, underpinned by transformer architectures and large-scale pre-training.
Semantic Classification
Content
Key Characteristics
-
Employs transformer models and attention mechanisms
-
Supports transfer learning through pre-trained language models
-
Handles multiple languages and cross-lingual transfer
-
Integrates linguistic knowledge and statistical learning
-
Enables few-shot and zero-shot learning paradigms
Overview
Natural Language Processing (NLP) is the subfield of AI focused on enabling computers to understand, interpret, generate, and manipulate human language. Core tasks include text classification, named entity recognition, machine translation, sentiment analysis, question answering, and language generation. Modern NLP leverages transformer architectures (BERT, GPT, T5), pre-training on massive corpora, and fine-tuning for downstream tasks. Advanced systems perform multimodal understanding (text-image), multilingual processing, and exhibit emergent capabilities like reasoning and code generation.
Related Concepts
-
References
-
Devlin, J. et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL 2019.
-
Brown, T. et al. (2020). Language Models are Few-Shot Learners. NeurIPS 2020.
-
Vaswani, A. et al. (2017). Attention is All You Need. NeurIPS 2017.