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Intro
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What is NLP
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Types of data
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Natural language processing
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Text data
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Rulebased approach
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NLP use cases
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NLP use case
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NLP summaries
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About Alexander
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About Koenigsberg
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What is Spacy
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Tokenization
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Rules
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Language Models
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Python
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Pipelines
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Visualization
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Serialization
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Danger Zones
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No Record Reference
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Comparison to other tools
Description:
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.

15 Things You Should Know About Spacy

EuroPython Conference
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