Applied Language Technology

  • Course level: All Levels


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 I Learn?

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

Topics for this course

48 Lessons

Applied Language Technology

#1 A brief introduction to JupyterLab00:00:00
#2 Cloning repositories from GitHub00:00:00
#3 Exploring Jupyter Notebooks00:00:00
#4 Getting started with Python: variables and objects00:00:00
#5 Text formats and encoding00:00:00
#6 Loading text into Python00:00:00
#7 Working with multiple files00:00:00
#8 Regular expressions00:00:00
#9 Loading a language model into spaCy00:00:00
#10 Providing text as input to a spaCy language model00:00:00
#11 Tokenizing text using spaCy00:00:00
#12 Part-of-speech tagging using spaCy00:00:00
#13 Morphological analysis using spaCy00:00:00
#14 Parsing and visualising syntactic dependencies using spaCy00:00:00
#15 Sentence segmentation using spaCy00:00:00
#16 Lemmatizing text using spaCy00:00:00
#17 Customizing spaCy pipelines00:00:00
#18 Processing texts efficiently using spaCy00:00:00
#19 Adding custom attributes to spaCy objects00:00:00
#20 Writing spaCy annotations to disk00:00:00
#21 Gold standards and reliability00:00:00
#22 Evaluating performance: accuracy, precision, recall and F1-score00:00:00
#23 Loading data into pandas Data Frames00:00:00
#24 Examining pandas DataFrames00:00:00
#25 Extending pandas DataFrames00:00:00
#26 Saving and loading pandas DataFrames00:00:00
#27 How to make the most of the learning materials00:00:00
#28 Introducing Stanza: how to download language models and load them into the library00:00:00
#29 Processing text using Stanza00:00:00
#30 Processing texts efficiently using Stanza00:00:00
#31 Interfacing the spaCy and Stanza libraries using spacy-stanza00:00:00
#32 Creating spaCy Matchers00:00:00
#33 Defining pattern rules for spaCy Matcher00:00:00
#34 Matching morphological features using spaCy Matcher00:00:00
#35 Matching syntactic dependencies using spaCy DependencyMatcher00:00:00
#36 Introduction to the distributional hypothesis00:00:00
#37 Exploring the distributional hypothesis from a syntagmatic perspective00:00:00
#38 Exploring the distributional hypothesis from a paradigmatic perspective00:00:00
#39 Preparing the data for learning word embeddings00:00:00
#40 Creating a neural network for learning word embeddings00:00:00
#41 Training a neural network for learning word embeddings00:00:00
#42 Introduction to word embeddings in spaCy00:00:00
#43 Visualising word embeddings in spaCy using whatlies00:00:00
#44 Contextual word embeddings in spaCy00:00:00
#45 Introduction to the CoNLL-U annotation schema00:00:00
#46 Parsing CoNLL-U annotations using Python00:00:00
#47 Creating a spaCy Doc object manually00:00:00
#48 Grouping spaCy Spans into a SpanGroup00:00:00

Enrolment validity: Lifetime


  • PC with Internet access
  • Skills in Python Programming