by Aaron Kramer | Released March 2017 | ISBN: 9781491982044

Dependency grammar is a powerful way to represent syntactic relationships within a sentence. More sophisticated than bag-of-words representations, it's used in natural language processing tasks like feature engineering, opinion mining, information retrieval, and relation extraction. In this course, which is designed for basic to intermediate level Python programmers, you'll learn how to represent dependency grammar as an extension to valency grammar and use it with spaCy. Discover valency grammar and how it's used to express word relationships Understand dependency grammar as a typed extension to valency grammar Explore the expressivity, assumptions, and limitations of dependency grammar Learn how to traverse parses with spaCy for various applications Gain experience training a spaCy parser on a Twitter datasetAaron Kramer is a data scientist and engineer with Los Angeles based DataScience Inc. He is a spaCY contributor who holds a BA in Economics from Swarthmore College and is the author of multiple O'Reilly titles on the subject of natural language processing. Show and hide more Publisher resources Download Example Code

1. Introduction
   • Introduction 00:00:28
   • About The Author 00:01:00
   • Models Of Grammar 00:04:01
2. Dependency Grammar And Tagging
   • Dependency Grammar 00:05:23
   • Dependency Grammar With SpaCy Part - 1 00:04:22
   • Dependency Grammar With SpaCy Part - 2 00:03:59
3. Training
   • Training Your Own Parser 00:03:50
   • Wrap Up 00:00:47
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