Novel Approach towards Arabic Question Similarity Detection

Daoud, Mohammad

doi:10.1109/ictcs.2019.8923102

Cited by 4 publications

(6 citation statements)

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“…A few works in the literature have attempted to address this task for the Arabic language, and acceptable results have been achieved compared to other languages [44], [45], [46], [47], [48]. Due to the lack of relevant Arabic semantic corpora, rulebased approaches are currently used to achieve this task.…”

Section: ) Question Semantic Similaritymentioning

confidence: 99%

“…In the work of M. Daoud, a hybrid approach of both rulebased and supervised learning approaches was used to automatically detect the textual and semantic similarity between the questions according to their scope and type [44]. In his approach, the author used 600 Arabic questions selected from the FAQ pages of different United Nations organizations and the website Ejaaba.com 2 .…”

Section: ) Question Semantic Similaritymentioning

confidence: 99%

“…To the best of our knowledge, even with the existing Arabic works, the Arabic QA research field is still limited in terms of question type coverage [22], [38], [44], domain [35], [41], [45], and system performance. Indeed, most Arabic works have resolved the issue of answering factual questions, while few studies have resolved complicated questions.…”

Section: ) Question Semantic Similaritymentioning

confidence: 99%

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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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Section: ) Question Semantic Similaritymentioning

confidence: 99%

Section: ) Question Semantic Similaritymentioning

confidence: 99%

Section: ) Question Semantic Similaritymentioning

confidence: 99%

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Recent Developments in Arabic Conversational AI: A Literature Review

Fuad

Al-Yahya

2022

IEEE Access

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“…But the recommended system is not efficient for complex queries. Mohammad Daoud (12) addressed the automatic Arabic query similarity identification. An approach based on rules incorporating semantic and lexical similarity was proposed and implemented.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, other authors have identified many gaps in their work. Some of them even applied in their native language like Arabic sentence similarity (12) and Chinese sentence similarity (13) . Most of these authors have identified gaps like lower precision and accuracy, suitable for document similarity but not for question pair similarity, etc.…”

Section: Introductionmentioning

confidence: 99%

Identifying Similar Question Pairs Using Machine Learning Techniques

Anishaa¹,

Sathvika²,

Rawat³

2021

IJST

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Background/Objectives: Every day millions of people visit search engines like Quora, reedit, stack overflow, etc., the demand for new intelligent techniques is growing, to help individuals find better solutions. Methods: In our proposed system, the Quora datasets were filtered using SQLite which takes one-quarter of the time taken to pre-process the same dataset using existing approaches like python functions. We used machine learning techniques namely the Random Forest, Logistic Regression, Linear SVM (Support Vector Machine) and XGBoost to analyze and identify the most suitable model. Findings: The error log loss functions (0.887, 0.521, 0.654 and 0.357) of the above machine learning techniques were analyzed and compared respectively. The performance of XGBoost is the best among the other models, hence XGBoost is the most efficient model. Conclusion/Future Scope: It is concluded that XGBoost has outperformed other machine learning techniques discussed in the study. It is also found that pre-processing using SQLite has improved the response time. In the future, we would like to apply a similar approach for various other search engines that are available like reedit, stack overflow, etc. and one could ensemble the best models of each type (linear, tree-based, and neural network).

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