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

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education Partnerships for the goals

Academic Year 2019/2020

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 and managment
    • 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.
    Statistical analysis of linguistic data.
    • On the importance of quantitative analysis for linguistics.
    • Fundamentals of R statistical package.
    • Descriptive statistics.
    • Analytical/Inferential statistics.

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.
  • Gries, S. (2009). Statistics for Linguistics with R. De Gruyter.

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

Teaching methods

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

 

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.

 

 

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.

Links to further information

http://corpora.ficlit.unibo.it/TRAM/

Office hours

See the website of Fabio Tamburini