Subject

XSL Content

Applications (I): Understanding NLP

General details of the subject

Mode
Face-to-face degree course
Language
English

Description and contextualization of the subject

This course will introduce the most commonly used techniques to build applications based on NLP. 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 or Fake News Detection.

The course will have a practical focus (laboratories and practical tasks) learning to use readily available NLP toolkits (Spacy, Flair, Transformers, etc.) based on machine and deep learning in a multilingual and multi-domain setting. The aim is to acquire the required autonomy to solve practical problems by applying and developing technology based on Natural Language Processing.

Teaching staff

NameInstitutionCategoryDoctorTeaching profileAreaE-mail
AGERRI GASCON, RODRIGOUniversity of the Basque CountryInvestigador Ramón Y CajalDoctorNot bilingual** n o c o n s t a e l a r e a * ó " á r e a p r o v i s i o n a l"rodrigo.agerri@ehu.eus

Competencies

NameWeight
Ability to apply existing NLP multilingual tools (morphological, syntactic and semantic taggers).33.0 %
Ability to understand the main characteristics of human language which made NLP processing such a challenging endeavor.33.0 %
Ability to use existing applications in human language technology.33.0 %

Study types

TypeFace-to-face hoursNon face-to-face hoursTotal hours
Lecture-based101525
Applied computer-based groups203050

Training activities

NameHoursPercentage of classroom teaching
Computer work practice, laboratory, site visits, field trips, external visits50.040 %
Lectures25.040 %

Assessment systems

NameMinimum weightingMaximum weighting
Portfolio20.0 % 20.0 %
Practical tasks40.0 % 40.0 %
Presentations20.0 % 20.0 %
Written examination20.0 % 20.0 %

Learning outcomes of the subject

Being able to use, design and do research on NLP applications based on document classification and sequence labeling, from a multilingual and multi-domain (news, social networks) point of view.



To identify the required linguistic resources in order to adapt NLP applications for our own needs.



Autonomy in order to solve practical problems by applying NLP technology.



Ability to understand the main characteristics of human language which made NLP processing such a

challenging endeavor.





Ordinary call: orientations and renunciation

This course follows continuous evaluation and the activities required are the ones listed above. Any student wishing to change the module shall speak to the master director.

Extraordinary call: orientations and renunciation

For extraordinary evaluation a written exam (80%) and a final project covering the syllabus of the module (20%) will be required.

Temary

1. Introduction to NLP: Practical Applications.

2. Multilingual Text Classification: Sentiment Analysis, Fake News, Stance and Propaganda detection in news and social networks.

3. Sequence Labeling: Named Entity Recognition, Contextual Lemmatization, Aspect Based Sentiment Analysis (ABSA).

4. Reformulating Sequence Labelling tasks.

Bibliography

Compulsory materials

Bing Liu. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012. https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.html







Natural Language Processing in Python (http://www.nltk.org/book)



O¿Brien, S., Balling, L. W., Carl, M., Simar, M. and Specia, L. 2014. Post-editing of Machine Translation. Cambridge Scholars Publishing.



Basic bibliography

Speech and Language Processing (3rd ed. draft). Dan Jurafsky and James H. Martin, 2021.



Bing Liu. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, 2012. https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.html



Natural Language Processing in Python (http://www.nltk.org/book)

O¿Brien, S., Balling, L. W., Carl, M., Simar, M. and Specia, L. 2014. Post-editing of Machine Translation. Cambridge Scholars Publishing.