# Hierarchical Text Classification of Urdu News using Deep Neural Network

arXiv: https://arxiv.org/abs/2107.03141 · HTML: https://mlsanity.com/paper/2107.03141/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [HDLTex: Hierarchical Deep Learning for Text Classification](https://mlsanity.com/paper/1709.08267/) (2017)
- [Weakly-Supervised Hierarchical Text Classification](https://mlsanity.com/paper/1812.11270/) (2018)
- [Hierarchical Text Classification with Reinforced Label Assignment](https://mlsanity.com/paper/1908.10419/) (2019)
- [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://mlsanity.com/paper/1412.1058/) (2014)
- [A scikit-based Python environment for performing multi-label classification](https://mlsanity.com/paper/1702.01460/) (2017)
- [Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification](https://mlsanity.com/paper/2003.01345/) (2020)

## Built on by (most cited first, 4 of 4)

- [HiClass: a Python library for local hierarchical classification compatible with scikit-learn](https://mlsanity.com/paper/2112.06560/) (2021)
- [Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph](https://mlsanity.com/paper/2402.18610/) (2024)
- [Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction](https://mlsanity.com/paper/2405.18507/) (2024)
- [The Role of Orthographic Consistency in Multilingual Embedding Models for Text Classification in Arabic-Script Languages](https://mlsanity.com/paper/2507.18762/) (2025)

## Abstract (arXiv)

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.