Advanced Natural Language Processing with spaCy

Topics: - Extracting linguistic features: part-of-speech tags, dependencies, named entities (NER) - Working with pre-trained statistical models - Finding words and phrases matches with Matcher and PhraseMatcher phrase rules - SpaCy data structures: Doc, Token, Span, Vocab, Lexeme - Semantic similarities using word vectors - Writing custom pipeline components with extension attributes - Scaling up spaCy pipelines - Creating training data for spacy NLP statistical models - Training and updating spaCy neural network models

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