Applied Language Technology

Tutorials on YouTube

Table of Contents

• About this MOOC
  • About the courses and their goals
  • Information for students at Finnish higher education institutions
  • Information for other users
• Getting Started
  • An overview of the learning environments
  • How to use the learning environments in practice?
    • Log in to CSC Notebooks
    • Launch a server on CSC Notebooks
    • Interact with the server in JupyterLab
    • Check your exercises using TestMyCode
• Frequently Asked Questions
• Part I: A Minimal Introduction to Python
  • The elements of a Jupyter Notebook
  • Getting started with Python
    • Variables
    • Objects
• Part II: Working with Text in Python
  • Manipulating text using Python
    • Computers and text
    • Text encoding
    • Loading plain text files into Python
    • Manipulating text
  • Manipulating text at scale
    • Regular expressions
    • Processing multiple files
  • Processing texts using spaCy
    • Getting started
    • Performing basic NLP tasks using spaCy
  • Customising the spaCy pipeline
    • Modifying spaCy pipelines
    • Processing texts efficiently
    • Adding custom attributes to spaCy objects
    • Writing processed texts to disk
    • Simplifying output for noun phrases and named entities
  • Evaluating language models
    • What is a gold standard?
    • Measuring reliability manually
    • Cohen’s kappa as a measure of agreement
    • Evaluating the performance of language models
  • Managing textual data using pandas
    • Importing data to pandas
    • Examining DataFrames
    • Extending DataFrames
    • Saving DataFrames
• Part III: Natural Language Processing for Linguists
  • Processing diverse languages
    • Introduction
    • Stanza – a Python library for processing many languages
    • Interfacing Stanza with spaCy
  • Universal Dependencies
    • A brief introduction to Universal Dependencies
    • Basic assumptions behind Universal Dependencies
    • Understanding the annotation schema
    • Exploring syntactic dependencies using spaCy
    • A final word on evaluation
  • Finding linguistic patterns using spaCy
    • Finding patterns using spaCy Matchers
    • Examining matches in context using concordances
  • Introducing word embeddings
    • Background: the distributional hypothesis and word embeddings
    • Exploring the distributional hypothesis
    • Learning word embeddings
  • Word embeddings in spaCy
    • Using word embeddings in spaCy
    • Visualising word embeddings
    • Contextual word embeddings from Transformers
  • Working with discourse-level annotations
    • Introducing the CoNLL-U annotation schema
    • Adding discourse-level annotations to Doc objects
    • Converting CoNLL-U annotations into Doc objects
• Citation
• Resources