Introduction to Natural Language Processing with spaCy > 자유게시판

spaCy provides industrial-strength natural language processing for Python. Install spaCy and download language models for different languages. The nlp object processes text and returns a Doc container. Tokenization splits text into tokens with linguistic annotations. Part-of-speech tagging identifies word types: noun, verb, adjective. Named Entity Recognition extracts entities like persons, organizations, dates. Dependency parsing shows grammatical relationships between words. Lemmatization reduces words to base forms. Rule-based matching uses Token Matcher and PhraseMatcher for pattern matching. EntityRuler adds custom entity recognition rules. Text classification enables sentiment analysis and topic categorization. Similarity comparison between documents, spans, and tokens. Word vectors capture semantic meaning for similarity and analogies. Train custom models for domain-specific NLP tasks. Pipeline customization adds or removes components. DisplaCy visualizes dependency trees and entity annotations. Integration with BERT and other models. spaCy achieves production-level speed and accuracy. Python library is well-documented with extensive examples. spaCy is the leading NLP library for production applications.