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 for core applications such as
Opinion Mining,
Named Entity Recognition, Lemmatization, Fake News Detection and
Fact-checking 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.