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).

Student profile

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.


Text Classification

Fake News, Stance and Propaganda
. Fake News
. Hyperpartisanism
. Hate speech
. Fact-checking
. Stance
. Argumentation
LABORATORY: Stance Detection
. Training with Flair and Spacy

Sequence Labelling

Named Entity Recognition
. Contextual Word Representations
. Datasets
. Evaluation
. Contextual and neural lemmatization
. Evaluation and application to high-inflected languages
LABORATORY: Train language independent neural sequence taggers
. Named Entity Recognition
. Contextual lemmatization.

Opinion Mining

Fine-grained Sentiment Analysis
Aspect-based Sentiment Analysis
Multidomain and multilingual issues
Sentiment Analysis
. Text Classification
Opinion Targets and Aspects
. Sequence Labelling

Question Answering

Redefining NLP tasks as QA
Pre-trained language models, Transformers
Multilingual transfer learning
Last words
LABORATORY Build and train a Question Answering system.


Person 1

Rodrigo Agerri

Ramon y Cajal researcher, member of Ixa
and HiTZ

Person 2

Joseba Fernandez de Landa

FPI researcher, member of Ixa
and HiTZ

Person 3

Iker Garcia

FPI researcher, member of Ixa
and HiTZ

Practical details

General information

Bring your own laptop (for the lab work).

Part of the Language Analysis and Processing master program.
5 theoretical sessions with corresponding programming labs (20 hours).
July 11th to 15th 2022, 15:00-19:00.

Where: "Ada Lovelace", Faculty of Informatics, Donostia-San Sebastian
(practical classes will be held in labs, split groups according to attendance).
The university provides some limited information about accommodation in San Sebastian (Basque/Spanish) and the Basque Country (English).
Course language: English.
Capacity: 40 attendants (First-come first-served).
Cost: 184 euros (180 for UPV/EHU members).


Registration is closed on the 26th of June 2022 (or until room is full).
Please register by email to (subject "Registration to ILTAPP" and CC
Also for any enquiry you might have.
The university provides official certificates (for an additional 27.96 euros). Please apply AFTER completing the course.
UPV/EHU can provide invoices addressed to universities or companies. More details are provided after registration is made.

Basic Python programming experience.
Not a requirement but, previous attendance to the Deep Learning for Natural Language Processing course held the previous week will help students to better understand the underlying algorithms of Language Technology applications.
Bring your own laptop (no need to install anything).

Class of July 2022 with a handful of the participants.

Class of July 2021 online :)