  In order to provide benchmark performance for Urdu text document
classification, the contribution of this paper is manifold. First, it pro-vides
a publicly available benchmark dataset manually tagged against 6 classes.
Second, it investigates the performance impact of traditional machine learning
based Urdu text document classification methodologies by embedding 10
filter-based feature selection algorithms which have been widely used for other
languages. Third, for the very first time, it as-sesses the performance of
various deep learning based methodologies for Urdu text document
classification. In this regard, for experimentation, we adapt 10 deep learning
classification methodologies which have pro-duced best performance figures for
English text classification. Fourth, it also investigates the performance
impact of transfer learning by utiliz-ing Bidirectional Encoder Representations
from Transformers approach for Urdu language. Fifth, it evaluates the integrity
of a hybrid approach which combines traditional machine learning based feature
engineering and deep learning based automated feature engineering. Experimental
results show that feature selection approach named as Normalised Dif-ference
Measure along with Support Vector Machine outshines state-of-the-art
performance on two closed source benchmark datasets CLE Urdu Digest 1000k, and
CLE Urdu Digest 1Million with a significant margin of 32%, and 13%
respectively. Across all three datasets, Normalised Differ-ence Measure
outperforms other filter based feature selection algorithms as it significantly
uplifts the performance of all adopted machine learning, deep learning, and
hybrid approaches. The source code and presented dataset are available at
Github repository.
