  This paper presents the system descriptions submitted at the FIRE Shared Task
2021 on Urdu's Abusive and Threatening Language Detection Task. This challenge
aims at automatically identifying abusive and threatening tweets written in
Urdu. Our submitted results were selected for the third recognition at the
competition. This paper reports a non-exhaustive list of experiments that
allowed us to reach the submitted results. Moreover, after the result
declaration of the competition, we managed to attain even better results than
the submitted results. Our models achieved 0.8318 F1 score on Task A (Abusive
Language Detection for Urdu Tweets) and 0.4931 F1 score on Task B (Threatening
Language Detection for Urdu Tweets). Results show that Support Vector Machines
with stopwords removed, lemmatization applied, and features vector created by
the combinations of word n-grams for n=1,2,3 produced the best results for Task
A. For Task B, Support Vector Machines with stopwords removed, lemmatization
not applied, feature vector created from a pre-trained Urdu Word2Vec (on word
unigrams and bigrams), and making the dataset balanced using oversampling
technique produced the best results. The code is made available for
reproducibility.
