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1
Introduction
2
About spaCy
3
Why NLP projects fail
4
How to maximize the risk of NLP projects
5
Failure sucks
6
Hierarchy of needs
7
Circular dependency
8
iterative process
9
waterfall approach
10
Annotation example
11
Rulebased logic example
12
General approach
13
Workflow
14
Solution
15
Evaluation
16
Annotation Projects
17
The Solution
Description:
Explore strategies for successful Natural Language Processing (NLP) projects in this 27-minute EuroPython 2018 conference talk. Learn about iterative approaches, avoiding common pitfalls, and maximizing project success using spaCy and Prodigy. Discover techniques for developing effective annotation schemes, model architectures, and pipelines. Gain insights into commercial machine learning project challenges and how to address them through practical examples and workflow recommendations. Understand the importance of evaluation, annotation projects, and iterative processes in building robust NLP solutions.

Building New NLP Solutions with spaCy and Prodigy

EuroPython Conference
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