Discover 15 essential insights about spaCy in this informative EuroPython 2020 conference talk by Alexander Hendorf. Explore the capabilities of this free, open-source library designed for advanced Natural Language Processing (NLP) in Python, with a focus on production use. Learn about spaCy's inner workings, including tokenization, language models, pipelines, and visualization techniques. Gain valuable knowledge on how to effectively process and understand large text corpora, as well as potential pitfalls to avoid. Compare spaCy to other NLP tools and understand its strengths in various use cases. Whether you're new to NLP or an experienced practitioner, acquire practical tips to enhance your text processing applications and improve your understanding of spaCy's functionality.
Overview
Syllabus
Intro
What is NLP
Types of data
Natural language processing
Text data
Rulebased approach
NLP use cases
NLP use case
NLP summaries
About Alexander
About Koenigsberg
What is Spacy
Tokenization
Rules
Language Models
Python
Pipelines
Visualization
Serialization
Danger Zones
No Record Reference
Comparison to other tools
Taught by
EuroPython Conference
