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modern_practical_nlp's Introduction

Modern Practical Natural Language Processing

This course will cover how you can use NLP to do stuff.

There are four videos

  1. Overview and Converting Text to Vectors
  • For finding similar documents
  • "I have this document or text, what others talk about the same stuff?"
  • Video
  1. Learning with Vectors and Classification
  • For classifying documents
  • "I need to put these documents into buckets."
  • Video
  1. Visualizing
  • For seeing what document vectors look like in 3D space
  • "I need to quickly see what looks similar to what."
  • Video
  1. Sequence Generation and Extracting Pieces of Information from Text
  • For translation and document summarization, and for pulling out sentences and documents that talk about specific things
  • "I need every mention of a street address or business in Garland, Texas; and I need each document translated to Urdu."
  • Video

Additional Details

The idea is we make short videos that focus on the aspects of NLP that currently work well and are useful.

Speech-to-text now works pretty well, so these methods will also be useful for the audio portions of videos.

All code will be available on GitHub here https://github.com/jmugan/modern_practical_nlp

About Me, Jonathan Mugan

The Limits of NLP

Computers can't read

  • Reading requires mapping language to internal concepts grounded in behaving in the same general environment as the writer.
    • Computers don’t have those concepts.
    • Example: “I pulled the wagon.” Computers don’t know that wagons can carry things or that pulling exerts a gentle tension to the arm and leg muscles as one walks.

Computers can't write

  • Writing requires mapping internal concepts grounded in behaving in the same general environment as the expected reader.
    • Computers don’t have those concepts

NLP Works Around Computers Not Having the Experience or Conceptual Framework to Read and Write

  • NLP is about how to make natural language amenable to computation even though computers can’t read or write.
  • Representing text as vectors has transformed NLP in the last 10 years.
  • There are also symbolic methods that are practically useful; we will cover those too.

Additional Information on NLP, AI, and Their Limits and Promise

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