Classification of Bengali Questions Towards a Factoid Question Answering System

Monisha, Syeda Tamanna Alam; Sarker, Sourav; Nahid, Mahadi Hasan

doi:10.1109/icasert.2019.8934567

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The future trends in developing Bangla question answering systems include: developing algorithmic Bangla Information Retrieval System (BIRS) [225], classification of Bangla questions towards a factoid question answering system [226] Bangla spam and fake detection system's future research trends include: developing hateful speech detection in public Facebook pages and other social sites [227], misinformation detection on online social networks [228], developing of Bangla datasets for both of the spam and ham data and many more. However, most of the researches on Bangla spam and fake detection applications is now following machine learning and deep learning methods.…”

Section: Future Trends In Text Formatmentioning

confidence: 99%

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

et al. 2022

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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,

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Section: Future Trends In Text Formatmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The future trends in developing Bangla question answering systems include: developing algorithmic Bangla Information Retrieval System (BIRS) [201], classification of Bangla questions towards a factoid question answering system [202] Bangla spam and fake detection system's future research trends include: developing hateful speech detection in public Facebook pages and other social sites [203], misinformation detection on online social networks [204], developing of Bangla datasets for both of the spam and ham data and many more. However, most of the researches on Bangla spam and fake detection applications is now following machine learning and deep learning methods.…”

Section: Future Trends In Text Formatmentioning

confidence: 99%

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

Sen,

Fuad,

Islam

et al. 2021

Preprint

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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 study of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough review of 71 BNLP research papers and categorize them into 11 categories,

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“…The QA system can provide information about related questions. However, the collection of questions in the group with a text classification process that aims to categorize class specifications for questions such as topics, objects, and keywords [17]. Classification is the process of assigning objects to particular classes or categories, so that classification makes it easier for the system to use query syntax to get words that are used as commands to obtain information about data collections about Sumedang Larang [18].…”

Section: Introductionmentioning

confidence: 99%

Question Answering using Ontology for Sumedang Larang History with Support Vector Machine Based on Telegram Bot

Perangin-Angin

Baizal²,

Richasdy³

2022

mib

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Technological developments affect many aspects, one of which is historical education. History lessons can shape students' personalities and encourage an interest in historical knowledge. There are many stories from Indonesian history, one of which is the Sumedang Larang Kingdom. The Sumedang Larang Kingdom is one of the Islamic kingdoms in Pasundan. However, not many people know about this kingdom. The millennial generation is technologically advanced, so they can take advantage of technological advances to quickly introduce the history of Sumedang Larang. One of them utilizes the telegram bot using the Application Programming Interface (API), which can connect the system to the telegram platform. In addition, this technology can be used as a history learning attraction using the question answering system (QA). Our research aims to build a QA system that can introduce the history of Sumedang Larang to the millennial generation. Because this system uses ontology knowledge with concepts related to the Sumedang Larang domain, it can focus on the history of Sumedang Larang. Applying the support vector machine (SVM) algorithm to process classification text can make it easier to search for text categories. The test results show the performance of the SVM method with a test size parameter of 0.5, such as 74% and 78%. The performance test results are accuracy scores in the subject category and object classification.

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Classification of Bengali Questions Towards a Factoid Question Answering System

Cited by 6 publications

References 9 publications

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

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

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

Question Answering using Ontology for Sumedang Larang History with Support Vector Machine Based on Telegram Bot

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