Module Database Search
MODULE DESCRIPTOR | |||
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Module Title | |||
Information Retrieval | |||
Reference | CM4144 | Version | 1 |
Created | January 2024 | SCQF Level | SCQF 10 |
Approved | April 2024 | SCQF Points | 15 |
Amended | ECTS Points | 7.5 |
Aims of Module | |||
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To provide students with a comprehensive understanding of the main principles and practices underlying the retrieval, extraction and mining of text and other data using advanced analytical techniques, including recommender systems, to make business decisions. |
Learning Outcomes for Module | |
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On completion of this module, students are expected to be able to: | |
1 | Examine the main concepts involved in information retrieval. |
2 | Develop intelligent information retrieval systems. |
3 | Test the effectiveness of information retrieval systems. |
4 | Execute state-of-the-art techniques for web mining and natural language processing. |
5 | Develop a recommender system for a given purpose. |
Indicative Module Content |
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Information collection: crawling and document pre-processing. Information retrieval: document Indexing, similarity metrics and clustering. Web Analytics. Comparative analysis of information retrieval and visualisation methods. Text extraction, tokenisation, stemming, bag-of-words, n-gram, statistical language models, vector representations and topic models. Word sense disambiguation, phrase and named entity recognition, POS tagging, shallow parsing, syntax and dependency parsing. Document similarity, clustering and classification, information extraction, sentiment analysis using lexicon-based techniques. Case studies on text classification, topic modelling applied to news articles, intelligent search and browse, social media mining. Personalisation, recommendation, user modelling, and interactive smart information systems. |
Module Delivery |
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Lectures are used to deliver the main principles and techniques. Computing laboratories will be used to acquire and practise practical skills and reinforce knowledge from the lectures. |
Indicative Student Workload | Full Time | Part Time |
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Contact Hours | 30 | N/A |
Non-Contact Hours | 120 | N/A |
Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
TOTAL | 150 | N/A |
Actual Placement hours for professional, statutory or regulatory body |   |   |
ASSESSMENT PLAN | |||||
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If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
Component 1 | |||||
Type: | Practical Exam | Weighting: | 100% | Outcomes Assessed: | 1, 2, 3, 4, 5 |
Description: | A practical exam assessing knowledge and practical skills in information retrieval techniques and evaluation of their results. |
MODULE PERFORMANCE DESCRIPTOR | |
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Explanatory Text | |
The calculation of the overall grade for this module is based on 100% weighting of Component 1. To pass the module students should achieve grade D or better. | |
Module Grade | Minimum Requirements to achieve Module Grade: |
A | The student must achieve an A in C1. |
B | The student must achieve a B in C1. |
C | The student must achieve a C in C1. |
D | The student must achieve a D in C1. |
E | The student must achieve an E in C1. |
F | The student must achieve an F in C1. |
NS | Non-submission of work by published deadline or non-attendance for examination |
Module Requirements | |
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Prerequisites for Module | None. |
Corequisites for module | None. |
Precluded Modules | None. |
INDICATIVE BIBLIOGRAPHY | |
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1 | Web information retrieval. Ceri, Stefano, 2013 |
2 | Designing the search experience : the information architecture of discovery. Russell-Rose, Tony.; Tate, Tyler. 2013 |
3 | Information Retrieval Searching in the 21st Century. Goker, Ayse and Davies, John. 2009 |
4 | Artificial intelligence a modern approach. Russell, Stuart J. , Norvig, Peter, 2014. |
5 | Recommendation and search in social networks. Ulusoy, Ozgur, Tansel, Abdullah Uz, Arkun, Erol, 2015. |