Study-Unit Description

Study-Unit Description


TITLE Applied Natural Language Processing

LEVEL 05 - Postgraduate Modular Diploma or Degree Course


DEPARTMENT Artificial Intelligence

DESCRIPTION Natural language processing (NLP) is an important subfield of Artificial Intelligence. This study-unit will expose the student to different techniques and NLP libraries available so as to gain a hands on approach to processing language. These libraries offer inbuilt powerful processing functions which allow us to process large quantities of data to extract interesting information, such as sentiment analysis.

Topics include:

- Introduction to Applied NLP;
- Working with datasets and corpora;
- The linguistic annotation pipeline;
- Text and Document classification;
- Information extraction;
- Ontologies and knowledge representation;
- Sentiment Analysis;
- Question-Answering Systems;
- Dialogue Systems.

Study-Unit Aims:

The study-unit aims to provide the student with an applied view of Natural Language Processing, by combining the theoretical aspects of NLP into a hands-on environment, using various NLP libraries and corpora.

Learning Outcomes:

1. Knowledge & Understanding:

By the end of the study-unit the student will:

- Gain knowledge of the different NLP libraries that are available, such as NLTK, OpenNLP, Standford CoreNLP and GATE, and be able to compare them to decide what libraries are required for any given project;
- Have an understanding of what the different algorithms do;
- Know how to apply such libraries to data.

2. Skills:

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

- Analyse the requirements for NLP tasks and select the appropriate software libraries;
- Apply different techniques to corpora;
- Build a pipeline of text processing tools to facilitate language processing and understanding;
- Use libraries and tools such as NLTK, OpenNLP, Standford CoreNLP and GATE.

Main Text/s and any supplementary readings:

- Jurafsky, D. & J. H. Martin (2009). Speech and Language Processing. (2nd edition). Indiana: Prentice Hall.
- T. Mitchell (1998). Machine learning. McGraw Hill.
- Manning, C. D., and Schütze, H. (1999) Foundations of Statistical Natural Language Processing. MIT Press, Cambridge Massachusetts.
- Bird, S., Klein E. and Loper, E. (2009) Natural Language Processing with Python, O'Reilly.
- D. Maynard, K. Bontcheva, I. Augenstein. (2016) Natural Language Processing for the Semantic Web.

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

Assessment Component/s Assessment Due Resit Availability Weighting
Presentation (20 Minutes) SEM2 No 20%
Project SEM2 Yes 80%

LECTURER/S Claudia Borg (Co-ord.)
Andrea De Marco
Marc Tanti

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 study-unit description above applies to the academic year 2018/9, if study-unit is available during this academic year, and may be subject to change in subsequent years.