Arabic Question Classification Using Support Vector Machines and Convolutional Neural Networks

Aouichat, Asma; Ameur, Mohamed Seghir Hadj; Geussoum, Ahmed

doi:10.1007/978-3-319-91947-8_12

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…The SVM algorithm is frequently utilized in question classification tasks. In [19], an Arabic version of the TREC dataset is introduced and employed to train a two-stage classification model. The model involves using either the SVM, RF, or ME algorithm, followed by a CNN neural network.…”

Section: Related Workmentioning

confidence: 99%

Question Classification in Albanian Through Deep Learning Approaches

Trandafili¹,

Kote²,

Plepi³

2023

IJACSA

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In recent years, there is growing interest in intelligent conversation systems. In this context, Question Classification is an essential subtask in Question Answering systems that determines the question type, therefore, also the type of the answer. However, while there is abundant research for English, little research work has been carried out for other languages. In this paper we deal with classification of questions in the Albanian language which is considered a complex Indo-European language. We employ both machine learning and deep learning approaches on a large corpus in Albanian based on the six-class TREC dataset with approximately 5000 questions. Experiments with and without stop-words show that the impact of stop-words is significant in the accuracy of the classifier. Extensive comparison of algorithms for the task of question classification in Albanian show that deep learning algorithms outperform conventional machine learning approaches. To the best of our knowledge this is the first approach in literature for classifying questions in Albanian and the results are highly comparable to English.

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Section: Related Workmentioning

confidence: 99%

Question Classification in Albanian Through Deep Learning Approaches

Trandafili¹,

Kote²,

Plepi³

2023

IJACSA

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“…Indeed, many studies have focused on the "wh-" question types and have designed approaches for limited domains. A few works in the literature have attempted to address this task for the Arabic language and have achieved satisfactory results [37], [39], [38], [40], [41].…”

Section: ) Question Classification (Qc)mentioning

confidence: 99%

“…Their work suggests that future research should concentrate on building an efficient Arabic QA system using both word embeddings and ML methods. From a different point of view, another study showed that obtaining finer classification will be more efficient using a pre-trained word embedding [40]. In this study, a novel approach was proposed by combining an SVM and a convolutional neural network (CNN) to serve the Arabic QC task using word vectors.…”

Section: ) Question Classification (Qc)mentioning

confidence: 99%

Recent Developments in Arabic Conversational AI: A Literature Review

Fuad

Al-Yahya

2022

IEEE Access

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Conversational AI is one of the most active research areas in AI, and it has gained more attention from academia as well as industry. Given recent advancements in several conversational AI systems in addition to the availability of several datasets, the aim of this study is to explore the landscape of Arabic text-based conversational AI systems. In this work, we provide a thorough review of recent Arabic conversational AI systems. We group them into three categories based on their functionality: (1) questionanswering (QA) systems, (2) task-oriented dialogue systems (DS), and (3) chatbots. Furthermore, we describe the common datasets used in building and evaluating conversational AI systems in Arabic. Few surveys have targeted the conversational AI field for the Arabic language, and we aim to cover this gap with this study. Our contribution focuses on reviewing and analyzing the literature in the field and highlighting future research directions towards human-like conversational AI systems in Arabic.

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“…Islam et al [57] proposed a machine learning approach to classify the Bangla question-answer types using stochastic gradient descent. The authors used two-layer taxonomy [145], which has six coarse classes [146]: abbreviation, entity, description, human, location, numeric, and 50 finer classes [147]. The authors achieved an average precision of 0.95562 for coarse classes and 0.87646 for more advanced classes, and after eliminating stop words, they achieved an average precision of 0.92421 for coarse classes and 0.8412 for finer classes.…”

Section: ) Machine Learning Approachesmentioning

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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Arabic Question Classification Using Support Vector Machines and Convolutional Neural Networks

Cited by 13 publications

References 12 publications

Question Classification in Albanian Through Deep Learning Approaches

Question Classification in Albanian Through Deep Learning Approaches

Recent Developments in Arabic Conversational AI: A Literature Review

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

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