  Digital text is increasing day by day on the internet. It is very challenging
to classify a large and heterogeneous collection of data, which require
improved information processing methods to organize text. To classify large
size of corpus, one common approach is to use hierarchical text classification,
which aims to classify textual data in a hierarchical structure. Several
approaches have been proposed to tackle classification of text but most of the
research has been done on English language. This paper proposes a deep learning
model for hierarchical text classification of news in Urdu language -
consisting of 51,325 sentences from 8 online news websites belonging to the
following genres: Sports; Technology; and Entertainment. The objectives of this
paper are twofold: (1) to develop a large human-annotated dataset of news in
Urdu language for hierarchical text classification; and (2) to classify Urdu
news hierarchically using our proposed model based on LSTM mechanism named as
Hierarchical Multi-layer LSTMs (HMLSTM). Our model consists of two modules:
Text Representing Layer, for obtaining text representation in which we use
Word2vec embedding to transform the words to vector and Urdu Hierarchical LSTM
Layer (UHLSTML) an end-to-end fully connected deep LSTMs network to perform
automatic feature learning, we train one LSTM layer for each level of the class
hierarchy. We have performed extensive experiments on our self created dataset
named as Urdu News Dataset for Hierarchical Text Classification (UNDHTC). The
result shows that our proposed method is very effective for hierarchical text
classification and it outperforms baseline methods significantly and also
achieved good results as compare to deep neural model.
