69706 - Linguistic Computer Science (1) (LM)

Course contents

Course syllabus is built incrementally considering CFUs:

-------------------------------------------------
COURSE SYLLABUS FOR 6 CFU
-------------------------------------------------

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.
• Tokenisation and sentence splitting
  
• Computational Linguistics.
  • 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
    • Word & Sentence embeddings
      
      

-------------------------------------------------------------
ADDITIONAL TOPICS FOR 9 CFUs
-------------------------------------------------------------

NLP: Applications & Case Studies

• Solving downstream tasks with Large Language Models
• Prompting Pre-Trained Language Models
• Automatic Identification of Prosodic Prominence
  
• Stylometry & Dialectometrics.

-------------------------------------------------------------
ADDITIONAL TOPICS FOR 12 CFUs
-------------------------------------------------------------

Statistical analysis of linguistic data.

• On the importance of quantitative analysis for linguistics.
• Fundamentals of statistic package R.
• Descriptive statistics.
• Analytical/Inferential statistics.

 

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 chapters extracted from:
- Tamburini F. (2022). Neural Models for the Automatic Processing of Italian, Bologna: Pàtron.
- D. Jurafsky and J.H. Martin (in press). Speech and Language Processing, Prentice Hall. (3rd Edition DRAFT).
- James Briggs (2022). Natural Language Processing for Semantic Search.
- Gries S. (2008). Statistics for Linguistics with R. Mouton de Gruyter.
Slides, handouts and papers downloadable from the course web site https://corpora.ficlit.unibo.it/TAL/ .

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 laboratory sessions for 30/45/60 hours depending on CFUs.

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.

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.

Links to further information

https://corpora.ficlit.unibo.it/TAL

Office hours

See the website of Fabio Tamburini