Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

Sen, Ovishake; Fuad, Mohtasim; Islam, Md. Nazrul; Rabbi, Jakaria; Masud, Mehedi; Hasan, Md. Kamrul; Awal, Md. Abdul; Fime, Awal Ahmed; Fuad, Md. Tahmid Hasan; Sikder, Delowar; Iftee, Md. Akil Raihan

doi:10.1109/access.2022.3165563

Cited by 33 publications

(9 citation statements)

References 189 publications

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“…These methods provide accurate results. However, these studies are usually limited to spoken language classification and identification of specific languages [10], [11], [12], [13]. There is no standard technique that can serve as the gold standard for discriminating between different languages.…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach for Identifying and Discriminating Spoken Arabic Among Other Languages

Alashban

Alotaibi

2023

IEEE Access

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Spoken Language Identification (SLID) is an important step in speech-to-speech translation systems and multi-lingual automatic speech recognition. In recent research, deep learning mechanisms have been the prevailing approaches for spoken language identification. This paper aims to study, detect, and analyze spoken languages similar to Arabic in pronouncing certain words and then proposes a deep learningbased architecture, specifically the Bidirectional Long Short Term Memory (BLSTM), for spoken Arabic language identification and discrimination between these similar languages, namely, German, Spanish, French, and Russian, all of which are taken from Mozilla speech corpus languages. Additionally, our work involves a linguistic study of these considered languages. A total of ten thousand speakers are chosen for all five languages, and the BLSTM architecture is designed and implemented using acoustic signal features and applied to five experiments in this paper. The results show a precision of 98.97%, 98.73%, 98.47%, and 99.75% for identifying the spoken Arabic language separately along with German, Spanish, French, and Russian, respectively. Additionally, we achieved an average accuracy of 95.15% for discriminating between all these considered five languages in terms of the pronunciation of words. Our findings confirm that a BLSTM architecture is able to distinguish between observable similar pronunciations of words in considered languages.

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Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach for Identifying and Discriminating Spoken Arabic Among Other Languages

Alashban

Alotaibi

2023

IEEE Access

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“…Recurrent neural networks (RNNs) are another type of deep learning architecture that has been used for document categorization tasks [19]. RNNs are useful for modeling sequential data, such as text, and can learn the context and dependencies between words in a document.…”

Section: Related Workmentioning

confidence: 99%

Automatic Classification of Scanned Electronic University Documents using Deep Neural Networks with Conv2D Layers

Baimakhanova,

Zhumadillayeva,

Avdarsol

et al. 2023

IJACSA

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This paper proposes a novel approach for scanned document categorization using a deep neural network architecture. The proposed approach leverages the strengths of both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract features from the scanned documents and model the dependencies between words in the documents. The pre-processed documents are first fed into a CNN, which learns and extracts features from the images. The extracted features are then passed through an RNN, which models the sequential nature of the text. The RNN produces a probability distribution over the predefined categories, and the document is classified into the category with the highest probability. The proposed approach is evaluated on a dataset of scanned documents, where each document is categorized into one of four predefined categories. The experimental results demonstrate that the proposed approach achieves high accuracy and outperforms existing methods. The proposed approach achieves an overall accuracy of 97.3%, which is significantly higher than the existing methods' accuracy. Additionally, the proposed approach's performance was robust to variations in the quality of the scanned documents and the OCR accuracy. The contributions of this paper are twofold. Firstly, it proposes a novel approach for scanned document categorization using deep neural networks that leverages the strengths of CNNs and RNNs. Secondly; it demonstrates the effectiveness of the proposed approach on a dataset of scanned documents, highlighting its potential applications in various domains, such as information retrieval, data mining, and document management. The proposed approach can help organizations manage and analyze large volumes of data efficiently.

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“…Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and more recently, Transformer-based architectures like BERT and GPT have been pivotal in advancing term recognition tasks. These deep learning models excel in capturing intricate linguistic structures and contextual dependencies, thereby improving the accuracy and robustness of term recognition systems [8]. CNNs are adept at capturing local features in text data, making them suitable for tasks like identifying key phrases or terms within a sentence.…”

Section: Introductionmentioning

confidence: 99%

Research on Term Recognition in the Field of English Linguistics Based on Deep Learning Algorithms

Zhu

2024

jes

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Linguistic recognition, a fundamental aspect of natural language processing, has seen significant advancements with the integration of deep learning algorithms. his paper presents a novel approach leveraging deep learning algorithms, specifically an optimized Long Short-Term Memory (LSTM) model, for term recognition tasks in English linguistics. This paper presents a novel approach leveraging deep learning algorithms, particularly Weighted Genetic Optimization Deep Learning (WGODL), for term recognition tasks in English linguistics. his hybrid approach not only enhances model accuracy but also improves robustness and generalization, enabling accurate term recognition even in noisy or ambiguous linguistic contexts. The weighted genetic optimization mechanism strategically prioritizes relevant features during the learning process, further enhancing the model's ability to discern domain-specific terms. Additionally, qualitative analysis highlights the interpretability of learned representations, shedding light on the underlying linguistic structures captured by the model. By harnessing the sequential nature of language data, the proposed model integrates LSTM units with optimization techniques to effectively capture contextual dependencies and semantic relationships within text corpora. Through extensive experimentation on diverse linguistic datasets, our optimized LSTM model achieves superior term recognition accuracy, surpassing traditional methods by 8.3%.

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Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

Cited by 33 publications

References 189 publications

A Deep Learning Approach for Identifying and Discriminating Spoken Arabic Among Other Languages

A Deep Learning Approach for Identifying and Discriminating Spoken Arabic Among Other Languages

Automatic Classification of Scanned Electronic University Documents using Deep Neural Networks with Conv2D Layers

Research on Term Recognition in the Field of English Linguistics Based on Deep Learning Algorithms

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