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The University of Southampton
Courses

COMP3225 Natural Language Processing

Module Overview

This module gives students an introduction to natural language processing (NLP) algorithms and an understanding of how to implement NLP applications.

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • Key concepts, tools and approaches for handling textual data
  • The underlying algorithmic and linguistic basis for NLP techniques
  • Algorithms commonly used for NLP problems such as information extraction, machine translation, text summarization and question answering
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Be able to describe and discuss the different subareas of NLP
  • Understand the potential and limitations of NLP techniques within application areas
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Be able to process text corpora ready for application of NLP algorithms and techniques
  • Be able to implement NLP algorithms and techniques

Syllabus

Working with Text Corpora - Text Normalization - Regular Expressions - Evaluation Metrics and Linguistic Resources Vector Semantics and Embeddings - Lexical and Vector Semantics - TF-IDF - Word2Vec Language Modelling and Parts of Speech Tagging - Language Modelling - Parts of Speech Tagging Syntactic and Semantic Parsing - Syntactic parsing - Text Chunking - Dependency Parsing - Word Senses and WordNet Sequence Processing with Recurrent Neural Networks - Recurrent Neural Networks - Sequence Processing for NLP Applications - Managing Context using LSTM’s and GRU’s Information Extraction - Named Entity Recognition - Relation Extraction - Temporal, Event and Location Extraction Applications of NLP - Statistical Machine Translation - Text Summarization - Question Answering

Learning and Teaching

Teaching and learning methods

Lectures and formative laboratories.

TypeHours
Revision10
Completion of assessment task45
Lecture24
Wider reading or practice63
Specialist Laboratory 8
Total study time150

Resources & Reading list

Jurafsky and Martin (2009). Speech and Language Processing. 

Aurelien Geron (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 

S.Bird, E.Klein & E.Loper (2009). Natural Language Processing with Python. 

Assessment

Summative

MethodPercentage contribution
Coursework 25%
Examination 75%

Repeat

MethodPercentage contribution
Examination 100%

Referral

MethodPercentage contribution
Examination 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Pre-requisites

To study this module, you will need to have studied the following module(s):

CodeModule
COMP3223Foundations of Machine Learning
COMP3222Machine Learning Technologies
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