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Department of Computer Science
Summative Coursework Set Front Page

Module Title: Text Mining and Natural Language Processing
Module Code: CS3TM20
Lecturer responsible: Professor Xia Hong
Type of Assignment: Technical report
Individual / Group Assignment: Individual
Weighting of the Assignment: 50%
Page limit/Word count: 12 pages (excluding appendix)
Expected hours spent for this assignment: 8 hours

Items to be submitted: Individual report in PDF with commented code
Work to be submitted on-line via Blackboard Learn by: 18/3/2024 noon
Work will be marked and returned by: 15 working days after the submission deadline

NOTES
By submitting this work you are certifying that it is all your own work and that use of material from other sources has been properly and fully acknowledged in the text. You are also confirming that you have read and understood the University’s Statement of Academic Misconduct, available on the University web-pages.
If your work is submitted after the deadline, 10% of the maximum possible mark will be deducted for each working day (or part of) it is late. A mark of zero will be awarded if your work is submitted more than 5 working days late. You are strongly recommended to hand work in by the deadline as a late submission on one piece of work can impact on other work.
If you believe that you have a valid reason for failing to meet a deadline then you should complete an Extenuating Circumstances form and submit it to the Student Support Centre before the deadline, or as soon as is practicable afterwards, explaining why.

1. Assessment classifications
First Class (>= 70%) The coursework demonstrates:
Exceptional understanding of the principles of natural language processing
Solid knowledge of used techniques/algorithms for text processing and excellent technique skills in implementing these algorithms.
Comprehensive analysis of results from the implemented algorithms
Excellent presentation of the report
Upper Second (60-69%) The coursework demonstrates:
Good understanding of the principles of natural language processing
Appropriate use of techniques/algorithms for text processing and good technique skills in implementing these algorithms.
Good technical skills in implementing these algorithms with good result analysis.
Clear presentation of the report
Lower Second (50-59%) The coursework demonstrates:
Basic understanding of the principles of natural language processing
Basic use of algorithms in implementing these algorithms.
Moderate technical skills in implementation
Clear presentation of the report
Third (40-49%) The coursework demonstrates:
Satisfactory understanding of the principles of natural language processing
Satisfactory use of algorithms in implementing these algorithms.
Satisfactory technical skills in implementation.
Pass (35-39%) The coursework demonstrates:
Satisfactory understanding of the principles of natural language processing
Satisfactory knowledge to implementing these algorithms.
Fail (0-34%) The coursework fails to demonstrate understanding of NLP processing techniques and skills in implementing these techniques.

2. Assignment description

Summary:
A technical report is required. Please refer to both style guide and marking scheme.. It is expected that the following sections/items are included in the report.
Abstract
Introduction
Methodology
Results and discussion
Conclusion
References
Appendix

The original code (with detailed comments) should be attached at the end of the report as an appendix.

Task:
This report should describe your coursework of training a Logistic regression classifier based on two Newsgroups and predict the group label of your own two class data set. A skeleton code is provided in Blackboard Week 7 folder to assist your implementation. You will have own data set by typing student number at the beginning of this code.
You need to modify the code accordingly to achieve the task below.
1.Download your two Newsgroups based on your student number.
2.Apply NLP analysis methods of each linguistic level including morphology, lexicon, syntax, and semantics to process the input text and extract features.
Use tf-idf weighted unigram bag-of-words model as baseline model.
Add more text extraction methods (optional).
3.Train Logistic Regression as the classifier for the two class data sets.
Present the result of Newsgroup prediction using various evaluation metrics:
overall accuracy plus precision, recall and F-1 measure.
4.In your report, explain methodologies of text processing and classification that are used in your experiments.
5.In your report, discuss the results and the effectiveness of the method.
6.In your report, give references and citations adequately.

3. Assignment submission requirements
Additional information
To produce the formal report, you may refer to the “CS Style Guide for reports”, which is placed on the Blackboard under the folder “assessment coursework”.

Front page of the submission
(the following are compulsory)
Module Code:
Assignment report Title:
Student Number (e.g. 25098635):
Data set information derived from student number:
Example
Note: If your student number results two identical data sets, please add a small number to student number and try again.
Date (when the work completed):
Actual hrs spent for the assignment:
Assignment evaluation (3 key points):

4. Marking scheme
The report will be marked in 100 as the full mark. The distribution of the 100 marks is listed below.

Weighting Fail
(0.2) Pass
(0.4) Satisfactory
(0.5) Good (0.6) Very good
(0.8) Outstanding
(1)

Abstract (5%)

Introduction (10%)

Methodology (20%)
(Selected NLP processing techniques, logistic regression classifier, pipeline description)
Data experiments and evaluation supported by Appendix (50%)
Task
description (10%)
NLP analysis
processing
(10%)
Logistic model
classification (10%)
Evaluation and discussions
(20%)
Conclusion (5%)

Presentation (10%)*
(report writing, organization, references)