Extracting Linguistic Features
This chapter is a deep dive into the full power of spaCy. You will discover the linguistic features, including spaCy’s most used features such as the part-of-speech (POS) tagger, the dependency parser, the named entity recognizer, and merging/splitting features.
First, you’ll learn about the POS tag concept, how the spaCy POS tagger functions, and how to place POS tags into your natural-language understanding (NLU) applications. Next, you’ll learn a structured way to represent the sentence syntax through the dependency parser. You’ll learn about the dependency labels of spaCy and how to interpret the spaCy dependency labeler results with revealing examples. Then, you’ll learn a very important NLU concept that lies at the heart of many natural language processing (NLP) applications—named entity recognition (NER). We’ll go over examples of how to extract information from the text using NER. Finally, you...
