Experiment to Deployment NLP

Description

Today's main challenges in NLP are two-fold:

I share a bag of tricks ranging such including,

We will get a glimpse of spaCy as we do above.

Prerequisites: Exposure to typical ML challenges in a NLP context e.g. compute time, cleaning large datasets

Best suited for an intermediate audience

Topics marked with * can/will be skipped to honour time constraints

Introduction [2 min]
- Tools & Ideas: spaCy, displacy, textacy
- Context of what to expect today
Building fast NLP Pipelines [5 min]
- Why pipelines?
- Why pipelines are slow?
- Why you need faster, leaner pipelines for production systems?
- 1-line change to get a faster tokenization pipeline with spaCy
Custom Entity Recognition with spaCy [10 minutes]
- Writing rules specific to your dataset, and/or
- Using Language Modeling pretraining*
Challenge: Lack of Tagged QA (Question & Answer) Data [15 minutes]
- How do we generate QA from a free text corpus?
- Linguistics 101
- Making the Questions: Using Sentence Inflection
- Getting the quiz together in 1 place*
Wrap up with end to end demo of QA generation

Previous Talks:
- inMobi Tech Talks: A Nightmare on the LM Street; Slides
- Wingify DevFest: NLP for Indian Languages; Slides, Youtube
About me: I have written a book on Practical NLP for Developers, and won the NLP Kernel Prize from Kaggle. I maintain awesome-nlp - a repository of world's best NLP tools

I am a FastAI International Fellow, Part 2 for both 2018 & 2019.