Applied Language Technology

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About Course

Applied Language technology (also known as natural language processing) is the study of how computers can process and represent natural language, which includes all spoken, written, and signed languages around the world. Language technology is being employed in a wide range of real-world applications, from simple chores like spam filtering to complicated communicative scenarios like teleconferencing.

Applied Language technology has grown more accessible in recent years as a result of this growth. Python, for example, is a popular programming language that allows users to employ the most up-to-date language technology. Programming languages like Python, on the other hand, might be scary to newcomers. As a result, these courses are designed to provide a gradual, hands-on introduction to utilizing Python programming language to apply language technologies.

These Applied Language courses are built with newcomers in mind, with a focus on language and linguistics students, to empower them by making language technology accessible and clear. These learning tools emphasize text as the outcome of linguistic processes, which are intrinsically linked to language use in society, rather than treating it as data and a source of information to be retrieved. If you’re already familiar with language technology, these resources should help you widen your linguistic horizons.

 

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What Will You Learn?

  • Part 1: A Quick Overview of Python
  • Part 2: Using Python to Work with Text
  • Part 3: Linguists' Guide to Natural Language Processing

Course Content

Applied Language Technology

  • #1 A brief introduction to JupyterLab
    00:00
  • #2 Cloning repositories from GitHub
    00:00
  • #3 Exploring Jupyter Notebooks
    00:00
  • #4 Getting started with Python: variables and objects
    00:00
  • #5 Text formats and encoding
    00:00
  • #6 Loading text into Python
    00:00
  • #7 Working with multiple files
    00:00
  • #8 Regular expressions
    00:00
  • #9 Loading a language model into spaCy
    00:00
  • #10 Providing text as input to a spaCy language model
    00:00
  • #11 Tokenizing text using spaCy
    00:00
  • #12 Part-of-speech tagging using spaCy
    00:00
  • #13 Morphological analysis using spaCy
    00:00
  • #14 Parsing and visualising syntactic dependencies using spaCy
    00:00
  • #15 Sentence segmentation using spaCy
    00:00
  • #16 Lemmatizing text using spaCy
    00:00
  • #17 Customizing spaCy pipelines
    00:00
  • #18 Processing texts efficiently using spaCy
    00:00
  • #19 Adding custom attributes to spaCy objects
    00:00
  • #20 Writing spaCy annotations to disk
    00:00
  • #21 Gold standards and reliability
    00:00
  • #22 Evaluating performance: accuracy, precision, recall and F1-score
    00:00
  • #23 Loading data into pandas Data Frames
    00:00
  • #24 Examining pandas DataFrames
    00:00
  • #25 Extending pandas DataFrames
    00:00
  • #26 Saving and loading pandas DataFrames
    00:00
  • #27 How to make the most of the learning materials
    00:00
  • #28 Introducing Stanza: how to download language models and load them into the library
    00:00
  • #29 Processing text using Stanza
    00:00
  • #30 Processing texts efficiently using Stanza
    00:00
  • #31 Interfacing the spaCy and Stanza libraries using spacy-stanza
    00:00
  • #32 Creating spaCy Matchers
    00:00
  • #33 Defining pattern rules for spaCy Matcher
    00:00
  • #34 Matching morphological features using spaCy Matcher
    00:00
  • #35 Matching syntactic dependencies using spaCy DependencyMatcher
    00:00
  • #36 Introduction to the distributional hypothesis
    00:00
  • #37 Exploring the distributional hypothesis from a syntagmatic perspective
    00:00
  • #38 Exploring the distributional hypothesis from a paradigmatic perspective
    00:00
  • #39 Preparing the data for learning word embeddings
    00:00
  • #40 Creating a neural network for learning word embeddings
    00:00
  • #41 Training a neural network for learning word embeddings
    00:00
  • #42 Introduction to word embeddings in spaCy
    00:00
  • #43 Visualising word embeddings in spaCy using whatlies
    00:00
  • #44 Contextual word embeddings in spaCy
    00:00
  • #45 Introduction to the CoNLL-U annotation schema
    00:00
  • #46 Parsing CoNLL-U annotations using Python
    00:00
  • #47 Creating a spaCy Doc object manually
    00:00
  • #48 Grouping spaCy Spans into a SpanGroup
    00:00

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