| CODE | ARI5121 | ||||||||
| TITLE | Applied Natural Language Processing | ||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||
| MQF LEVEL | 7 | ||||||||
| ECTS CREDITS | 5 | ||||||||
| 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; - Processing both text and speech data; - Text and Document classification; - Information extraction; - 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. The unit will cover different techniques, including machine learning and deep learning techniques applied to NLP. 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, , and be able 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 and other libraries. 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. |
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| STUDY-UNIT TYPE | Ind Study, Lecture, Ind Online Learning & Project | ||||||||
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| LECTURER/S | Claudia Borg (Co-ord.) Andrea De Marco Marc Tanti |
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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. |
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