Participation

3 - Set exercises - 10

Group coursework

1 - Set exercises - 50

Individual assignment

2 - Set exercises - 40

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The topics covered include:  
<ol><li>String parsing and tokenisation </li><li>Part of speech tagging </li><li>Automatic summarization </li><li>Relationship extraction </li><li>Semantic and topic modelling </li><li>Statistical models to predict and assign linguistic features </li></ol>
The lectures will contain toolkits to deal with a wide class of business and policy problems which require to get patterns, trends, and insights out of fluid, unstructured natural language data. The coursework will require to carry out NLP tasks on real-world datasets using cutting-edge and widely diffused modules for Python (e.g.,Gensim, NLTK, spaCy, Tensor Flow).

Feedback is provided in a number of ways throughout your module. This includes feedback you receive during teaching from both peers and lecturers and that provided for formative assessment. Following an assessment, you will be given your marks and feedback in line with the assessment regulations and policy. More information on the timing and type of feedback that will be provided for each assessment will be available from the module leader. <a href='https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations'>https://www.citystgeorges.ac.uk/about/governance/policies/student-policies-and-regulations</a>

This module will support the development of all the graduate attributes. Your module leader will provide you with information about how your learning, teaching and assessment activities support your development of these attributes. The graduate attributes are: - Always learning - Engaged in the world - Technical and digital - Connected Professional - Creating Impact

Directed Asynchronous - 

Self-directed - 

Synchronous online - 

Text as data: A new framework for machine learning and the social sciences. 

Blueprints for Text Analytics Using Python.

 Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition.

 Natural language processing with transformers.

SMM694 - Applied Natural Language Processing (NLP)

SMM694-PRD3-ONL-ALV1

Lectures, case studies, computer-based work and individual study should be expected 
Self-directed study time accounts for the minimum amount of time you should spend studying independently.  For example, carrying out subject research, practising and reflecting on techniques learned in class, reading widely around the subject to develop a deeper understanding.

Contact hours are designed to be flexible and are delivered using a variety of methods to help you learn in the most effective way. A proportion of the core content may be delivered online and additional support materials and content will also be made available through Moodle.

Applied Natural Language Processing (NLP)

Carry out data NLP tasks using popular Python modules; 

Skills

Evaluate at a high level, semantic and topic modelling schemas guiding the quantitative analysis of textual data. 

Knowledge

Identify and apply relevant NLP techniques; 

Frame NLP problems from an applied data science perspective and be able to suggest practical solutions. 

Embrace technical challenges as an opportunity for personal development. 

Values and attitudes

Extend NLP knowledge through searching for and using appropriate resources. 

Analyse the breadth of NLP approaches and techniques; 

Appreciate the wide scope of modern approaches to the quantitative analysis of textual data and algorithms to solve analytics problems; 

Communicate with data and computer scientists and understand their approach to problem solving in their very own terms; 

Show an interest in increasing your understanding and knowledge of how NLP can be applied in various fields; 