29853 - Computational Linguistics (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

At the end of this course the student will be able to manage some of the main topics in Natural Language Processing placing particular emphasis on advanced empirical methods used for linguistic analysis.

Course contents

Introduction

  • Natural Language Processing - Problems and perspectives
  • Basic corpus linguistics
  • Fundamentals of probability calculus
  • N-grams and language models

Natural Language Processing

  • Machine learning techniques.
  • Methods for evaluating application performances in Computational Linguistics.
  • Linguistics from a computational point of view.
    • COMPUTATIONAL PHONETICS
      • Audio sample properties - phones and formants
        Frequency Analysis - Spectrograms - Soprasegmental phenomena.
      • Applications for speech processing.
    • COMPUTATIONAL MORPHOLOGY
      • Generation and morphological analysis. Tabular lexica.
      • Techniques based on Finite State Automata.
    • COMPUTATIONAL SYNTAX
      • Part-of-speech tagging
      • Grammars for natural language
      • Parsing natural languages
      • Formal grammars for language analysis
        • Formal languages and natural language
        • Context-free grammars
        • Dependency grammars
        • Treebanks
    • COMPUTATIONAL SEMANTICS
      • Lexical semantics: WordNet, FrameNet...
      • Word Sense Disambiguation
      • Distributional Semantic Models
      • Sentence semantincs and meaning representation

Applications:
  • Case study: automatic identification of prosodic prominence
  • Stylometry
  • Dialectometry

Readings/Bibliography

Some chapters extracted from:
- Lenci, A., Montemagni, S. and Pirrelli, V. (2005). Testo e computer. Carocci.
- D. Jurafsky and J.H. Martin (2008). Speech and Language Processing, 2nd ed., Prentice Hall.
- A. Clark, C. Fox, S. Lappin (2010). The Handbook of Computational Linguistics and Natural Language Processing, Blackwell Handbooks in Linguistics.
Slides, handouts and papers downloadable from the course web site http://corpora.ficlit.unibo.it/LingCompLM/ [http://corpora.ficlit.unibo.it/LingCompLM/]


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 laboratory sessions for 30 hours.

Assessment methods

The exam consists of an oral colloquium on the course contents 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 [https://almaesami.unibo.it/almaesami/welcome.htm] 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/LingCompLM/

Office hours

See the website of Fabio Tamburini