Write a report (max 1,500 words) on the challenges the dataset presents, the
solutions, and your findings, which will be assessed as follows:
1) Discuss the following feature extraction techniques and explain how they
work and their advantages and disadvantages
a) Term Frequency-Inverse Document Frequency (TF-IDF) [10%]
b) BERT [10%]
2) Two step Classification:
a) Related/Unrelated classification:
i) Use TF-IDF features to train a standard Machine Learning model
(e.g. SVM, Naïve Bayes, Random Forest), and discuss its
performance on the testing set to classify whether the article body is
related or unrelated to the headline. [15%]
ii) Train one Deep Learning model (e.g., LSTM, RNN, CNN). Explain
and justify the architecture of the deep learning model, the hyper-
parameters used, and the loss function. Discuss the performance on
the testing set to classify whether the article body is related or
unrelated to the headline. [15%]
iii) Analyse and compare the performance results for the two models.
[10%]
b) Agree/Disagree/Discuss classification:
i) Build a new deep learning model of your choice to classify articles
into the remaining three categories (Agree/Disagree/Discuss). The
inputs to this model should be only samples that are related to the
headline (i.e. you should train and test your model on only these
samples). [15%].
ii) Analyse the performance of your model and report the results. [10%]
3) What are the ethical implications of your proposed solutions? What are
the potential biases and future misuse cases? [10%]
4) Academic English writing, with good use of technical vocabulary, correct
grammar, appropriate document structure and referencing where relevant.
[5%]
You should submit your 1,500-word report and also the associated Jupyter
notebook used to produce your analysis and graphs.
