Study-Unit Description

Study-Unit Description


CODE ARI2203

 
TITLE Statistical Natural Language Processing

 
UM LEVEL 02 - Years 2, 3 in Modular Undergraduate Course

 
MQF LEVEL 5

 
ECTS CREDITS 4

 
DEPARTMENT Artificial Intelligence

 
DESCRIPTION Natural Language Processing (NLP) is the study of the computational treatment of human languages with a particular focus on the interaction between computers and humans using our natural language. It is the driving force behind many applications, like virtual assistants, chatbots, sentiment analysis, automatic summarization, machine translation and more.

This study-unit will provide an introduction to NLP and the type of machine learning algorithms that are used to process both text and speech data. The processing of language is a rather challenging task since language is inherently ambiguous and complex. This study-unit will expose the different challenges within the NLP field and the ways that statistical machine learning approaches are used.

Study-Unit Aims:

The study-unit aims to:

- Provide students with a clear knowledge-base and understanding of the different problems faced in Natural Language Processing;
- Taking a problem-solving approach to these different challenges;
- Discussing the state-of–the–art, with a special focus to Robotics and Artificial Intelligence – what is required for humans to interact seamlessly with machines using Natural Language such as English or Maltese?

Learning Outcomes:

1. Knowledge & Understanding

By the end of the study-unit the student will be able to:

- Have a clear idea of the challenges in the field of Natural Language Processing;
- Understand the concepts and terminology in Natural Language Processing, such as N-Gram models, Hidden Markov and Maximum Entropy Models, and more;
- Understand better the requirements of processing large volumes of data.

2. Skills

By the end of the study-unit the student will be able to:

- Process text and speech data for the purpose of linguistic annotation and processing;
- Propose machine learning algorithms for the processing of text and speech data;
- Implement standard statistical NLP algorithms.

Main Text/s and any supplementary readings:

- D. Jurafsky and J.H. Martin (2009). Speech and Language Processing (2nd edition). New Jersey: Prentice Hall.
- T. Mitchell (1998). Machine learning. McGraw Hill.
- Foundations of Statistical Natural Language Processing (1999) by Christopher D. Manning and Hinrich Schütze.
- S. Bird, E. Klein and E. Loper, Natural Language Processing with Python, R'Reilly, 2009.

 
STUDY-UNIT TYPE Ind Study, Lecture, Ind Online Learning & Project

 
METHOD OF ASSESSMENT
Assessment Component/s Assessment Due Sept. Asst Session Weighting
Project SEM2 Yes 100%

 
LECTURER/S Kurt Abela
Claudia Borg (Co-ord.)
Andrea De Marco
Ingrid Galea

 

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years.

https://www.um.edu.mt/course/studyunit