Study-Unit Description

Study-Unit Description


TITLE Natural Language Processing: Methods and Tools

LEVEL 02 - Years 2, 3 in Modular Undergraduate Course


DEPARTMENT Artificial Intelligence

DESCRIPTION Natural Language Processing (NLP) covers a broad spectrum of subjects and approaches concerned with the goal of creating “language enabled” computer programs that can process natural language in ways inspired by humans. Very roughly, approaches to NLP can be divided into (a) the rational approach, where the aim is to equip the computer with knowledge about language in the form of rules, and (b) the empirical approach, where the starting point is raw linguistic data and a variety of data-driven methods are used to analyse it.

This study-unit will provide an introduction to NLP concepts by exposing the student to both of these approaches, with a main emphasis on the data-driven methods used in both text and speech processing.

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 solutions for the processing of text and speech data;
- Implement standard 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.
- Pereira, Fernando C. N., and Stuart M. Shieber. 1987. Prolog and natural-language analysis. In CSLI Lecture Notes Number 10. Menlo Park, CA: Center for the Study of Language and Information. 266 pages.
- S. Bird, E. Klein and E. Loper, Natural Language Processing with Python, O'Reilly, 2009.

STUDY-UNIT TYPE Blended Learning

Assessment Component/s Assessment Due Resit Availability Weighting
Project SEM2 Yes 100%


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It should be noted that all the information in the study-unit description above applies to the academic year 2019/0, if study-unit is available during this academic year, and may be subject to change in subsequent years.