Muhammad Raafey Tariq

Academic

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Verbatim text

Comparative Analysis of Different Word Embedding Techniques 
 
In this project, we trained different word-embedding models using the Word2Vec, GloVe, fastText, 
ELMo and BERT techniques on Urdu and Roman Urdu corpora. Each model was trained using the 
same set of parameters. The embeddings learnt by these models were evaluated using a number of 
Natural Language Processing tasks. Our comparison consisted of both a Qualitative Analysis as well 
as a Quantitative Analysis. 
For the Qualitative analysis, we plotted clusters of words regarded to be similar by these models 
and drew conclusions based on some characteristics of those graphs. For the Quantitative Analysis, we used the models to perform a Word Similarity task on the WordSim-353 and SimLex-999 
benchmark datasets. A Spearman’s Correlation Coefficient was calculated between the similarity 
scores given in the datasets and the ones generated by these models. 
Furthermore, the Roman Urdu models were also evaluated using a Sentiment Analysis task over a 
Roman Urdu Tweets dataset. The embeddings from these models were fed to a Neural Network 
which was fine-tuned for this task. The BERT model trained over Urdu was also evaluated using a 
Sentence Classification task on the XNLI dataset. Our model was able to reach the baseline score of 
56.6, which was achieved by the Google Research Group’s Urdu model. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Technology Used: 
Python, TensorFlow, Gensim,  
Google Colab, Google Cloud 
Supervisor Name: 
Ms. Mehreen Alam 
Group Members:   
Abubakar Ijaz (i16 - 0123) 
Ali Nauman Qureshi (i16 - 0138) 
Muhammad Raafey Tariq (i16 - 0259)

AI enrichment