85450 - Text Retrieval, Analysis and Mining (1) (LM)

Learning outcomes

The course aims at initiating to techniques for texts manipulation. At the end of the course the student knows how to process texts using computational tools, how to retrieve and extract information from large text corpora, how to annotate texts with linguistic information, how to classify texts and perform topic modelling and how to manage social media texts for mining information, opinions and sentiments.

Course contents

Techniques for Corpus Creation, Managment and Retrieval
• Corpus linguistics: representativeness, annotations and querying. The Zipf's law. Web as a corpus.
  
• Tokenisation and sentence splitting.
• Methods for Text Retrieval.
• Regular expressions.
• Multimodal annotations: annotation graph.
• XML corpora.
• Corpus querying packages.
• Case studies:
  • Written ans spoken corpora (Italian/English): a review.
  • Corpora@FICLIT: CORIS/CODIS, BoLC and DiaCORIS.
AI and Machine Learning for Textual Analysis.

• Introduction to AI & ML.
• Supervised and Unsupervised ML.
• Organisation of a ML experiment and Evaluation.
• Practical ML experiments on texts with Orange.

 

Students with SLD or temporary or permanent disabilities. It is suggested that they get in touch as soon as possible with the relevant University office (https://site.unibo.it/studenti-con-disabilita-e-dsa/en) and with the lecturer in order to seek together the most effective strategies for following the lessons and/or preparing for the examination.

Readings/Bibliography

Some sections extracted from:

• McEnery T., Wilson A. (2001). Corpus Linguistics, Edinburgh University Press.
• D. Jurafsky and J.H. Martin (2008). Speech and Language Processing, Prentice Hall.
• Tamburini F. (2022). Neural Models for the Automatic Processing of Italian, Bologna: Pàtron.
• Neapolitan, Jiang (2018). Artificial Intelligence. Chapman and Hall.
• Bengio, Courville, Goodfellow (2016). Deep Learning. The MIT Press.

Slides, handouts and papers downloadable from the course web site.

The course contents for students not attending the lessons are the same. However, students not able to attend the lessons are strongly invited to contact the teacher to get some explanations and avoid any misunderstanding about the course contents and reading materials.

Teaching methods

Face-to-face classes and labs for 30 hours.

In consideration of the type of activity and the teaching methods adopted, the attendance of this training activity requires the prior participation of all students in the training modules 1 and 2 on safety in the study places, in e-learning mode.

Assessment methods

An oral colloquium consisting of at least three questions on the course contents.

The oral colloquium is designed to evaluate the critical skills and methodological knowledge gained by the student.
Reaching a clear view of all the course topics as well as using a correct language terminology will be valued with maximum rankings. Mnemonic knowledge of the course topics or not completely appropriate terminology will be valued with intermediate rankings. Unknown topics or inappropriate terminology use will be valued, depending on the seriousness of the omissions, with minimal or insufficient rankings.

 

It is compulsory to register for the exam using the online procedure.

Students with SLD or temporary or permanent disabilities. It is necessary to contact the relevant University office (https://site.unibo.it/studenti-con-disabilita-e-dsa/en) with ample time in advance: the office will propose some adjustments, which must in any case be submitted 15 days in advance to the lecturer, who will assess the appropriateness of these in relation to the teaching objectives.

Teaching tools

The course web site is the central point for any kind of information about the course. It contains the handouts and the readings discussed during the lessons as well as a rich software repository useful for laboratory practice.

Office hours

See the website of Fabio Tamburini