Applied Language Technology

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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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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About Course

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Course Content

Applied Language Technology

#1 A brief introduction to JupyterLab

#2 Cloning repositories from GitHub

#3 Exploring Jupyter Notebooks

#4 Getting started with Python: variables and objects

#5 Text formats and encoding

#6 Loading text into Python

#7 Working with multiple files

#8 Regular expressions

#9 Loading a language model into spaCy

#10 Providing text as input to a spaCy language model

#11 Tokenizing text using spaCy

#12 Part-of-speech tagging using spaCy

#13 Morphological analysis using spaCy

#14 Parsing and visualising syntactic dependencies using spaCy

#15 Sentence segmentation using spaCy

#16 Lemmatizing text using spaCy

#17 Customizing spaCy pipelines

#18 Processing texts efficiently using spaCy

#19 Adding custom attributes to spaCy objects

#20 Writing spaCy annotations to disk

#21 Gold standards and reliability

#22 Evaluating performance: accuracy, precision, recall and F1-score

#23 Loading data into pandas Data Frames

#24 Examining pandas DataFrames

#25 Extending pandas DataFrames

#26 Saving and loading pandas DataFrames

#27 How to make the most of the learning materials

#28 Introducing Stanza: how to download language models and load them into the library

#29 Processing text using Stanza

#30 Processing texts efficiently using Stanza

#31 Interfacing the spaCy and Stanza libraries using spacy-stanza

#32 Creating spaCy Matchers

#33 Defining pattern rules for spaCy Matcher

#34 Matching morphological features using spaCy Matcher

#35 Matching syntactic dependencies using spaCy DependencyMatcher

#36 Introduction to the distributional hypothesis

#37 Exploring the distributional hypothesis from a syntagmatic perspective

#38 Exploring the distributional hypothesis from a paradigmatic perspective

#39 Preparing the data for learning word embeddings

#40 Creating a neural network for learning word embeddings

#41 Training a neural network for learning word embeddings

#42 Introduction to word embeddings in spaCy

#43 Visualising word embeddings in spaCy using whatlies

#44 Contextual word embeddings in spaCy

#45 Introduction to the CoNLL-U annotation schema

#46 Parsing CoNLL-U annotations using Python

#47 Creating a spaCy Doc object manually

#48 Grouping spaCy Spans into a SpanGroup

Student Ratings & Reviews