Introduction to Applications of Language Technology
Cross-lingual Information Extraction
LABORATORY: Stance detection with logistic regression
. Static Word Embeddings
Introduction to Flair
Introduction to Spacy
Language Technology is increasingly present in many of the applications we use in our everyday activities (Google Home, Amazon Alexa, Siri, Google Translate, Grammar checkers, Google search engine...) and the need of experts that can develop applications based on Language Technology is an ever growing demand both in the industry and academia. This course will introduce the most commonly used techniques to build applications based on Language Technology. Thus, the attendees will learn how to apply techniques such as document classification, sequence labeling, as well as vector-based word representations (embeddings) and pretrained language models (T5, GPT, etc.) for core applications such as Opinion Mining, Named Entity Recognition, Fake News Detection or Question Answering.
The course will have a practical focus (laboratories and practical tasks) learning to use readily available LT toolkits (Spacy, Flair, Transformers) based on machine and deep learning in a multilingual and multi-domain setting. The aim is to allow attendees to acquire the required autonomy to solve practical problems by applying and developing Language Technology applications. The course will be taught in English.
The course is part of the NLP master hosted by the Ixa NLP research group at the HiTZ research center of the University of the Basque Country (UPV/EHU).
This course is targeted to graduate students and professionals from a range of disciplines (linguistics, journalism, computer science, sociology, etc.) that need an applied introduction to Language Technology. This involves identifying the required linguistic resources, appropriate tools/libraries and techniques with the aim of acquiring the required autonomy to solve practical problems by applying and developing applications based on Language Technology in different and creative ways.For the practical content (coding exercises) some experience in python programming is recommended. Previous attendance to the Deep Learning for Natural Language Processing course might be useful although not required.