The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive review of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough analysis of 75 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50% of the papers were published after 2015. Furthermore, we discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP. 

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

The Word Sense Disambiguation (WSD) is a process of disambiguating the sense of the text according to its context. Machine translation is one of the challenging task since it requires effective rep...

Machine translation model for effective translation of Hindi poetries into English

An effective automatic traffic sign classification and recognition deep convolutional networks

The area of computer vision is emerging continually with the increasing interaction and development to provide a comfortable interaction between human and machines. One of the key aspects in the process of computer vision is object detection. Either objects can be identified partially or close to the original objects. The accuracy in detecting the objects can be improved by using state-of-the-art deep learning models like faster-Regional Convoluted Neural Network (faster-RCNN), You Only Look Once model (YOLO), Single Shot Detector (SSD) etc. Traditional algorithms can’t recognize objects as efficiently due to its limitations. Whereas the deep learning models require large amount of data for training the dataset, which has more resource and labour intensive in nature. The selection of algorithm determines its precision in object detection as well as its reliability. The recognition and classification of object begins with preparing dataset followed by splitting the dataset into training dataset and test dataset. The task of training the dataset can be assisted by both traditional as well as modern deep neural networks. The loss per step or epoch is calculated on the training dataset to signify the efficiency and accuracy of the model. In this model, the loss per step is 2.73. We have achieved a maximum accuracy of about 85.18% after training the dataset used.

Object Detection for Autonomous Vehicle Using TensorFlow

The process of identifying the meaning of a polysemous word correctly from a given context is known as the Word Sense Disambiguation (WSD) in natural language processing (NLP). Adapted Lesk algorithm based system is proposed which makes use of knowledge based approach. This work utilizes WordNet as the knowledge source (lexical database). The proposed system has three units – Input query, Pre-Processing and WSD classifier. Task of input query is to take the inputs sentence (which is an unstructured query) from the user and render it to the pre-processing unit. Pre-processing unit will convert the received unstructured query into a structured query by adding some features such as Part of Speech (POS) tagging, grammatical identification (Subject, Verb, and Object) and this structured query is transferred to the WSD classifier. WSD classifier uniquely identifies the sense of the polysemous word using the context information of the query and the lexical database.

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Adapted Lesk Algorithm based Word Sense Disambiguation using the Context Information

Traffic sign classification is an important aspect of autonomous driving systems. A slight improvement on classification performance can potentially lower the rate of car accidents. In view of this, we propose three different deep convolutional neural networks in a hierarchical pattern, yet not convoluted among themselves for classifying traffic sign. A very popular and reliable traffic sign dataset called GTSRB is used to train our proposed networks. In our work, we present a novel approach to classify images. Furthermore, we modify all three convolutional neural networks over some of the existing neural nets. While modifying the networks, we redesign them based on specific requirements which may also prove handy for other datasets. Along with the new methods, we are able to reduce the computational complexity as well. On top of the new architecture, we achieve a notably higher accuracy in performance of 99.92% surpassing the state-of-the-art performance of 99.81%. In a nutshell, we trained an artificial intelligence (AI) model that learns to chose between two different AI models while classifying an image.

An Efficient Traffic Sign Recognition Approach Using a Novel Deep Neural Network Selection Architecture

Word Sense Disambiguation (WSD) is the task of determining the specific meaning of an ambiguous word according to the context. In the realm of natural language processing, WSD is an open problem, and its development can significantly assist in human-level machine translation. In this paper, we have proposed a system for Bengali Word Sense Disambiguation through the FP-Growth algorithm. Moreover, we have also implemented the Apriori algorithm and presented an analysis on both of them to explain how the FP-Growth algorithm outperforms the Apriori algorithm performing Bengali Word Sense Disambiguation. Furthermore, In the testing phase we have found that for 80% of the test sentences, our proposed method can retrieve the exact meaning of an ambiguous word.

Word Sense Disambiguation of Bengali Words using FP-Growth Algorithm

Multiclass Classification for GvHD Prognosis Prior to Allogeneic Stem Cell Transplantation

Liver Disease Classification by Pruning Data Dependency Utilizing Ensemble Learning Based Feature Selection

There are several tasks that human excel at and computers do not and vice-versa. Just until a few years ago computers were as good as a storage for images and videos. However, in the past 6 years with the boon in artificial neural network, labeled data and computation power; machines have started becoming smart at tasks like recognizing images, detecting different objects in images, captioning images, understanding and summarizing videos, detecting semantic actions in videos and so on. Deep learning researchers and practitioners have started demonstrating notable performance of AI(Artificial Intelligence) on many different tasks that pushes the boundaries and as a continuation of that process, we took one specific problem to solve using deep learning that even human can not solve. We have taken Bangla handwritten characters, then trained them applying several deep learning techniques such as Convolutional Neural Network and Recurrent Neural Network to predict the sex of the writer. Consequently, we have got 91.85% accuracy rate and also demonstrated further analysis of the results that we got.

Md. Asif Bin Khaled

Papers

An Efficient Traffic Sign Recognition Approach Using a Novel Deep Neural Network Selection Architecture

Word Sense Disambiguation of Bengali Words using FP-Growth Algorithm

Multiclass Classification for GvHD Prognosis Prior to Allogeneic Stem Cell Transplantation

Liver Disease Classification by Pruning Data Dependency Utilizing Ensemble Learning Based Feature Selection

Detecting Sex From Handwritten Examples