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  3. Natural Language Processing with spaCy
  • 1

    Introduction to NLP and spaCy

    Gratuito

    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.

  • 2

    spaCy Linguistic Annotations and Word Vectors

    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.

  • 3

    Data Analysis with spaCy

    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.

  • 4

    Customizing spaCy Models

    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.

Initializing

Exercicio

Word-sense disambiguation with spaCy

WSD is a classical problem of deciding in which sense a word is used in a sentence. Determining the sense of the word can be crucial in search engines, machine translation, and question-answering systems. In this exercise, you will practice using POS tagging for word-sense disambiguation.

There are two sentences containing the word jam, with two different senses and you are tasked to identify the POS tags to help you determine the corresponding sense of the word in a given sentence.

The two sentences are available in the texts list. The en_core_web_sm model is already loaded and available for your use as nlp.

Instruções 1/2

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  • Create a documents list containing the Doc containers of each element in the texts list.
  • Print a tuple of the token's text and POS tags per each Doc container only if the word jam is in the token text.