Research in Natural Language Processing (NLP) has increasingly become
important due to applications such as text classification, text mining,
sentiment analysis, POS tagging, named entity recognition, textual entailment,
and many others. This paper introduces several machine and deep learning
methods with manual and automatic labelling for news classification in the
Bangla language. We implemented several machine (ML) and deep learning (DL)
algorithms. The ML algorithms are Logistic Regression (LR), Stochastic Gradient
Descent (SGD), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest
Neighbour (KNN), used with Bag of Words (BoW), Term Frequency-Inverse Document
Frequency (TF-IDF), and Doc2Vec embedding models. The DL algorithms are Long
Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit
(GRU), and Convolutional Neural Network (CNN), used with Word2vec, Glove, and
FastText word embedding models. We develop automatic labelling methods using
Latent Dirichlet Allocation (LDA) and investigate the performance of
single-label and multi-label article classification methods. To investigate
performance, we developed from scratch Potrika, the largest and the most
extensive dataset for news classification in the Bangla language, comprising
185.51 million words and 12.57 million sentences contained in 664,880 news
articles in eight distinct categories, curated from six popular online news
portals in Bangladesh for the period 2014-2020. GRU and Fasttext with 91.83%
achieve the highest accuracy for manually-labelled data. For the automatic
labelling case, KNN and Doc2Vec at 57.72% and 75% achieve the highest accuracy
for single-label and multi-label data, respectively. The methods developed in
this paper are expected to advance research in Bangla and other languages.

本文介绍了几种机器和深度学习方法，应用于孟加拉语新闻分类。这些方法包括逻辑回归、支持向量机、卷积神经网络以及词向量等，同时提出了自动标记的方法，并在孟加拉语最大的新闻分类数据集 Potrika 上评估了它们的性能。

使用手动和自动标注的机器学习和深度学习方法用于孟加拉语新闻分类

Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language

Knowledge is central to human and scientific developments. Natural Language
Processing (NLP) allows automated analysis and creation of knowledge. Data is a
crucial NLP and machine learning ingredient. The scarcity of open datasets is a
well-known problem in machine and deep learning research. This is very much the
case for textual NLP datasets in English and other major world languages. For
the Bangla language, the situation is even more challenging and the number of
large datasets for NLP research is practically nil. We hereby present Potrika,
a large single-label Bangla news article textual dataset curated for NLP
research from six popular online news portals in Bangladesh (Jugantor,
Jaijaidin, Ittefaq, Kaler Kontho, Inqilab, and Somoyer Alo) for the period
2014-2020. The articles are classified into eight distinct categories
(National, Sports, International, Entertainment, Economy, Education, Politics,
and Science \& Technology) providing five attributes (News Article, Category,
Headline, Publication Date, and Newspaper Source). The raw dataset contains
185.51 million words and 12.57 million sentences contained in 664,880 news
articles. Moreover, using NLP augmentation techniques, we create from the raw
(unbalanced) dataset another (balanced) dataset comprising 320,000 news
articles with 40,000 articles in each of the eight news categories. Potrika
contains both the datasets (raw and balanced) to suit a wide range of NLP
research. By far, to the best of our knowledge, Potrika is the largest and the
most extensive dataset for news classification.

该论文介绍了一种名为 Potrika 的大型单标签 Bangla 新闻文章文本数据集，包括八个区分不同类别的分类属性，为 NLP 研究提供了两个不平衡和平衡的数据集，以适应广泛的 NLP 研究，并且是目前最大、最全面的新闻分类数据集。

Potrika: 八个话题和五个属性的孟加拉语原始平衡报纸数据集

Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with Eight Topics and Five Attributes

The recent advances in Natural Language Processing have only been a boon for
well represented languages, negating research in lesser known global languages.
This is in part due to the availability of curated data and research resources.
One of the current challenges concerning low-resourced languages are clear
guidelines on the collection, curation and preparation of datasets for
different use-cases. In this work, we take on the task of creating two datasets
that are focused on news headlines (i.e short text) for Setswana and Sepedi and
the creation of a news topic classification task from these datasets. In this
study, we document our work, propose baselines for classification, and
investigate an approach on data augmentation better suited to low-resourced
languages in order to improve the performance of the classifiers.

研究了自然语言处理技术在低资源语言中的应用，提出了用于塞茨瓦纳语和塞班语头条新闻的数据集及其用于新闻主题分类的基线模型，并探索了一种适合低资源语言的数据增强方法以提高分类器性能。