Linguïstische annotaties in spaCy

Linguïstische annotaties in spaCy, zoals POS-tagging, named-entity recognition en dependency parsing, kun je gebruiken om de schrijfkwaliteit beter te begrijpen en meer te leren over allerlei aspecten van menselijke taal, zoals woorden, zinnen en betekenis. In deze oefening ga je oefenen met verschillende linguïstische kenmerken.

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Natural Language Processing met spaCy

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Deze oefening maakt deel uit van de cursus

Natural Language Processing met spaCy

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This chapter will introduce you to NLP, some of its use cases such as named-entity recognition and AI-powered chatbots. You’ll learn how to use the powerful spaCy library to perform various natural language processing tasks such as tokenization, sentence segmentation, POS tagging, and named entity recognition.

Exercise 1: Natural Language Processing (NLP) basics Exercise 2: Doc container in spaCy Exercise 3: NER use case Exercise 4: Tokenization with spaCy Exercise 5: spaCy basics Exercise 6: Running a spaCy pipeline Exercise 7: Lemmatization with spaCy Exercise 8: Sentence segmentation with spaCy Exercise 9: Linguistic features in spaCy Exercise 10: POS tagging with spaCy Exercise 11: NER with spaCy Exercise 12: Text processing with spaCy

Learn about linguistic features, word vectors, semantic similarity, analogies, and word vector operations. In this chapter you’ll discover how to use spaCy to extract word vectors, categorize texts that are relevant to a given topic and find semantically similar terms to given words from a corpus or from a spaCy model vocabulary.

Exercise 1: Linguïstische kenmerken Exercise 2: Linguïstische annotaties in spaCy

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Exercise 3: Woordbetekenis-ontleding met spaCy Exercise 4: Dependency parsing met spaCy Exercise 5: Introductie tot woordvectoren Exercise 6: spaCy-woordenschat Exercise 7: Woordvectoren in de spaCy-woordenschat Exercise 8: Woordvectoren en spaCy Exercise 9: Analogieën en vectorbewerkingen Exercise 10: Projectie van woordvectoren Exercise 11: Soortgelijke woorden in een vocabulaire Exercise 12: Semantische overeenkomsten meten met spaCy Exercise 13: Doc-overeenkomst met spaCy Exercise 14: Span-overeenkomst met spaCy Exercise 15: Semantische gelijkenis voor het categoriseren van tekst

Get familiar with spaCy pipeline components, how to add a pipeline component, and analyze the NLP pipeline. You will also learn about multiple approaches for rule-based information extraction using EntityRuler, Matcher, and PhraseMatcher classes in spaCy and RegEx Python package.

Exercise 1: spaCy pipelines Exercise 2: Adding pipes in spaCy Exercise 3: Analyzing pipelines in spaCy Exercise 4: spaCy EntityRuler Exercise 5: EntityRuler with blank spaCy model Exercise 6: EntityRuler for NER Exercise 7: EntityRuler with multi-patterns in spaCy Exercise 8: RegEx with spaCy Exercise 9: RegEx in Python Exercise 10: RegEx with EntityRuler in spaCy Exercise 11: spaCy Matcher and PhraseMatcher Exercise 12: Matching a single term in spaCy Exercise 13: PhraseMatcher in spaCy Exercise 14: Matching with extended syntax in spaCy

Explore multiple real-world use cases where spaCy models may fail and learn how to train them further to improve model performance. You’ll be introduced to spaCy training steps and understand how to train an existing spaCy model or from scratch, and evaluate the model at the inference time.

Exercise 1: Customizing spaCy models Exercise 2: Training spaCy models Exercise 3: Model performance on your data Exercise 4: spaCy training data format Exercise 5: Training steps Exercise 6: Annotation and preparing training data Exercise 7: Compatible training data Exercise 8: Training with spaCy Exercise 9: Training preparation steps Exercise 10: Train an existing NER model Exercise 11: Training a spaCy model from scratch Exercise 12: Wrap-up