Natural Language Processing
Natural Language Processing (often referred to as 'NLP') is the application of computational techniques to the analysis and synthesis of natural language and speech.
Resources
Training New Language Models in spaCy
ENThis course covers practices and workflows for training new NLP models using spaCy, from project setup to model evaluation and packaging.Cadet: Preparing Data for New Language Models in spaCy
ENMISSING GOALS OF THIS COURSE __ ONE SENTENCE:spaCy Architecture for Humanists
ENThis course introduces the architecture of the spaCy NLP library and its implications for humanists who want to use it in their research.Machine Learning for NLP
ENThis course provides a conceptual introduction to machine learning and its role in Natural Language Processing.NLP for Humanists: An Introduction to Key Concepts and Workflows
ENThe goal of this course is to introduce key concepts and workflows in Natural Language Processing (NLP) to humanities scholars who have little or no experience with the field.Finding Places in Text with the World Historical Gazetteer
ENResearchers often need to be able to search a corpus of texts for a defined list of terms and historians are often interested in certain places named in a text or texts. This lesson details how to programmatically search documents for a list of terms, including place names and then how to obtain coordinates and map historical place names with the World Historical Gazetteer.Digital Historical Research on European Historical Newspaper with NewsEye Platform
ENSince their beginnings in the 17th century, newspapers have recorded billions of events, stories and personal names in almost every language and every country daily. This course from DariahTeach provides an introduction to digitised historical newspaper analysis, incorporating methods of Natural Language Processing for discovering, exploiting and visualising newspapers.Introduction to Programming for NLP with Python
ENThe aim of this virtual course is to offer basic knowledge and skills in programming in Python. Target audiences are undergraduate and graduate students in the Humanities and Social Sciences who want to acquire hands-on knowledge and skills in working with textual data or quantitative data in language and humanities research.