Text classification has been one of the earliest problems in NLP. Over time the scope of application areas has broadened and the difficulty of dealing with new areas (e.g., noisy social media content) has increased. The problem-solving strategy switched from classical machine learning to deep learning algorithms. One of the recent deep neural network architecture is the Transformer. Models designed with this type of network and its variants recently showed their success in many downstream natural language processing tasks, especially for resource-rich languages, e.g., English. However, these models have not been explored fully for Bangla text classification tasks. In this work, we fine-tune multilingual transformer models for Bangla text classification tasks in different domains, including sentiment analysis, emotion detection, news categorization, and authorship attribution. We obtain the state of the art results on six benchmark datasets, improving upon the previous results by 5-29% accuracy across different tasks.
Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus
School of Management Science and Engineering, Shandong University of Finance and Economics（山东财经大学管理科学与工程学院）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）
CineXDrama: Relevance Detection and Sentiment Analysis of Bangla YouTube Comments on Movie-Drama using Transformers: Insights from Interpretability Tool
Ahsanullah University Of Science and Technology (AUST)（阿萨努拉科技大学（AUST））
University of Liberal Arts Bangladesh (ULAB)（孟加拉国自由艺术大学）
Sentiment Analysis (SA) is an action research area in the digital age. With rapid and constant growth of online social media sites and services, and the increasing amount of textual data such as - statuses, comments, reviews etc. available in them, application of automatic SA is on the rise. However, most of the research works on SA in natural language processing (NLP) are based on English language. Despite being the sixth most widely spoken language in the world, Bangla still does not have a large and standard dataset. Because of this, recent research works in Bangla have failed to produce re…
Shahjalal University of Science and Technology（沙赫贾拉尔科技大学）
Language models are at the core of natural language processing. The ability to represent natural language gives rise to its applications in numerous NLP tasks including text classification, summarization, and translation. Research in this area is very limited in Bangla due to the scarcity of resources, except for some count-based models and very recent neural language models being proposed, which are all based on words and limited in practical tasks due to their high perplexity. This paper attempts to approach this issue of perplexity and proposes a subword level neural language model with the…
Depression detection from Social Media Bangla Text Using Recurrent Neural Networks
University of Maryland Baltimore County（马里兰大学巴尔的摩分校）；Bangladesh University of Engineering & Technology（孟加拉国工程技术大学）；University of Dhaka（达卡大学）；Jahangirnagar University（贾汉吉尔纳加尔大学）；Jagannath University（贾格纳特大学）
Shahjalal University of Science and Technology（沙赫贾拉勒科技大学）
In this paper, we describe a research method that generates Bangla word clusters on the basis of relating to meaning in language and contextual similarity. The importance of word clustering is in parts of speech (POS) tagging, word sense disambiguation, text classification, recommender system, spell checker, grammar checker, knowledge discover and for many others Natural Language Processing (NLP) applications. In the history of word clustering, English and some other languages have already implemented some methods on word clustering efficiently. But due to lack of the resources, word clusterin…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
This paper presents a method for detecting grammatical errors in Bangla using a Text-to-Text Transfer Transformer (T5) Language Model, using the small variant of BanglaT5, fine-tuned on a corpus of 9385 sentences where errors were bracketed by the dedicated demarcation symbol. The T5 model was primarily designed for translation and is not specifically designed for this task, so extensive post-processing was necessary to adapt it to the task of error detection. Our experiments show that the T5 model can achieve low Levenshtein Distance in detecting grammatical errors in Bangla, but post-process…
Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Cognitive Insight Limited（认知洞察有限公司）；BJIT Limited（BJIT有限公司）；Dhaka University（达卡大学）
Bangla -- ranked as the 6th most widely spoken language across the world (https://www.ethnologue.com/guides/ethnologue200), with 230 million native speakers -- is still considered as a low-resource language in the natural language processing (NLP) community. With three decades of research, Bangla NLP (BNLP) is still lagging behind mainly due to the scarcity of resources and the challenges that come with it. There is sparse work in different areas of BNLP; however, a thorough survey reporting previous work and recent advances is yet to be done. In this study, we first provide a review of Bangla…
Restoring Rhythm: Punctuation Restoration Using Transformer Models for Bangla, A Low-Resource Language
Bangladesh Army International University of Science and Technology (BAIUST)（巴尔克理工大学）；BRAC University（BRAC大学）；North East University Bangladesh (NEUB)（北东大学）
Understanding Public Perception of Crime in Bangladesh: A Transformer-Based Approach with Explainability
Chittagong University of Engineering and Technology（吉大港工程技术大学）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；European University of Bangladesh（孟加拉国欧洲大学）
A Unified BERT-CNN-BiLSTM Framework for Simultaneous Headline Classification and Sentiment Analysis of Bangla News
Department of CCE, International Islamic University Chittagong（国际伊斯兰大学丘拉图加冈分校计算机工程系）；Department of ICE, Noakhali Science and Technology University（诺阿克利科学与技术大学信息工程系）；Department of CSE, Banladesh University of Engineering and Technology（孟加拉工程与技术大学计算机科学与工程系）；Dept. of CSE, IIUC, Chattogram, Bangladesh（国际伊斯兰大学丘拉图加冈分校计算机科学与工程系）；School of CSE, University of Southern Mississippi（密西西比大学南方分校计算机科学学院）；Department of CSE, Netrokona University（纳特罗克纳大学计算机科学与工程系）；mPower Social Enterprise（mPower社会企业）
University of Saarland（萨尔大学）；University of Chittagong（吉大港大学）
Detecting emotions from text is an extension of simple sentiment polarity detection. Instead of considering only positive or negative sentiments, emotions are conveyed using more tangible manner; thus, they can be expressed as many shades of gray. This paper manifests the results of our experimentation for fine-grained emotion analysis on Bangla text. We gathered and annotated a text corpus consisting of user comments from several Facebook groups regarding socio-economic and political issues, and we made efforts to extract the basic emotions (sadness, happiness, disgust, surprise, fear, anger)…
The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
The global world is crossing a pandemic situation where this is a catastrophic outbreak of Respiratory Syndrome recognized as COVID-19. This is a global threat all over the 212 countries that people every day meet with mighty situations. On the contrary, thousands of infected people live rich in mountains. Mental health is also affected by this worldwide coronavirus situation. Due to this situation online sources made a communicative place that common people shares their opinion in any agenda. Such as affected news related positive and negative, financial issues, country and family crisis, lac…
Universal Networking Language (UNL) is a declarative formal language that is used to represent semantic data extracted from natural language texts. This paper presents a novel approach to converting Bangla natural language text into UNL using a method known as Predicate Preserving Parser (PPP) technique. PPP performs morphological, syntactic and semantic, and lexical analysis of text synchronously. This analysis produces a semantic-net like structure represented using UNL. We demonstrate how Bangla texts are analyzed following the PPP technique to produce UNL documents which can then be transl…
Cognitive Insight Limited（认知洞察有限公司）；Qatar Computing Research Institute, HBKU（卡塔尔计算研究所，卡塔尔 Hamad Bin Khalifa University）
Sentiment analysis has been widely used to understand our views on social and political agendas or user experiences over a product. It is one of the cores and well-researched areas in NLP. However, for low-resource languages, like Bangla, one of the prominent challenge is the lack of resources. Another important limitation, in the current literature for Bangla, is the absence of comparable results due to the lack of a well-defined train/test split. In this study, we explore several publicly available sentiment labeled datasets and designed classifiers using both classical and deep learning alg…
Beyond Clean Text: Evaluating Encoder and Decoder Robustness for Bangla Event Detection in Noisy Text
Jahangirnagar University（贾汉吉尔纳加尔大学）；Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
Breaking the Silence: A Dataset and Benchmark for Bangla Text-to-Gloss Translation
IIT, University of Dhaka, Bangladesh（达卡大学理工学院，孟加拉国）；University of Maryland, Baltimore County, USA（马里兰大学巴尔的摩县分校，美国）；SUNY, Old Westbury, USA（SUNY，美国奥尔德韦斯特伯里）
Sylhet Engineering College（锡尔赫特工程学院）；Shahjalal University of Science & Technology（沙赫贾拉勒科技大学）
Despite the success of the neural sequence-to-sequence model for abstractive text summarization, it has a few shortcomings, such as repeating inaccurate factual details and tending to repeat themselves. We propose a hybrid pointer generator network to solve the shortcomings of reproducing factual details inadequately and phrase repetition. We augment the attention-based sequence-to-sequence using a hybrid pointer generator network that can generate Out-of-Vocabulary words and enhance accuracy in reproducing authentic details and a coverage mechanism that discourages repetition. It produces a r…
Bangla BERT for Hyperpartisan News Detection: A Semi-Supervised and Explainable AI Approach
Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；Chittagong University of Engineering and Technology（吉大港工程技术大学）；European University of Bangladesh（孟加拉国欧洲大学）；Cosmic IT；Kidz Programming
Shahjalal University of Science and Technology（沙贾拉勒科技大学）
Characters are the smallest unit of text that can extract stylometric signals to determine the author of a text. In this paper, we investigate the effectiveness of character-level signals in Authorship Attribution of Bangla Literature and show that the results are promising but improvable. The time and memory efficiency of the proposed model is much higher than the word level counterparts but accuracy is 2-5% less than the best performing word-level models. Comparison of various word-based models is performed and shown that the proposed model performs increasingly better with larger datasets. …
Cross-Dialect NER for Bangla Regional Dialects Using Leave-One-Dialect-Out Cross-Validation and Explainable AI
Ahsanullah University of Science and Technology（阿赫萨努拉科技大学）；Southeast University（东南大学）；Technische Universität Dresden（德累斯顿工业大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
We introduce a new dataset for offline Handwritten Text Recognition (HTR) from images of Bangla scripts comprising words, lines, and document-level annotations. The BN-HTRd dataset is based on the BBC Bangla News corpus, meant to act as ground truth texts. These texts were subsequently used to generate the annotations that were filled out by people with their handwriting. Our dataset includes 788 images of handwritten pages produced by approximately 150 different writers. It can be adopted as a basis for various handwriting classification tasks such as end-to-end document recognition, word-spo…
BanglaASTE: A Novel Framework for Aspect-Sentiment-Opinion Extraction in Bangla E-commerce Reviews Using Ensemble Deep Learning
Department of Computer Science；Engineering Chittagong University of Engineering；Department of Information Technology Washington University of Science \& Technology 2900 Eisenhower Ave, Alexandria, VA 22314, United States；Department of Information Systems Central Michigan University 1200 S. Franklin St. Mount Pleasant, Mich. 48859
Hybrid Machine Learning Model for Detecting Bangla Smishing Text Using BERT and Character-Level CNN
Department of Computer Science and Engineering（计算机科学与工程系）；World University of Bangladesh（孟加拉世界大学）；Department of Electrical and Electronics Engineering（电气与电子工程系）；Jashore University of Science and Technology（贾沙尔大学科学与技术学院）；Southern University Bangladesh（孟加拉南部大学）；Institute of Information Technology（信息技术研究所）；Noakhali Science and Technology University（诺阿基利科学与技术大学）
Being the seventh most spoken language in the world, the use of the Bangla language online has increased in recent times. Hence, it has become very important to analyze Bangla text data to maintain a safe and harassment-free online place. The data that has been made accessible in this article has been gathered and marked from the comments of people in public posts by celebrities, government officials, athletes on Facebook. The total amount of collected comments is 44001. The dataset is compiled with the aim of developing the ability of machines to differentiate whether a comment is a bully exp…
