AlQuAnS – An Arabic Language Question Answering System

Nabil, Mohamed; Abdel-Megied, Ahmed E.-S.; Ayman, Yasmin; Fathy, Ahmed; Khairy, Ghada; Yousri, Mohammed; El-Makky, Nagwa M.; Nagi, Khaled

doi:10.5220/0006602901440154

Cited by 6 publications

(6 citation statements)

References 7 publications

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“…Each medium calls for a unique vocabulary and set of linguistic abilities. Word choice becomes more complicated when considering the subject matter and the target recipient ( Ismail & Homsi, 2018 ; Akour et al, 2011 ; Al-Khawaldeh, 2019 ; Bakari & Neji, 2022 ; Nabil et al, 2017 ; Fareed, Mousa & Elsisi, 2013 ).…”

Section: Resultsmentioning

confidence: 99%

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A comprehensive survey of techniques for developing an Arabic question answering system

Alkhurayyif

Sait

2023

PeerJ Computer Science

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The question-answering system (QAS) aims to produce a response to a query using information from a text corpus. Arabic is a complex language. However, it has more than 450 million native speakers across the globe. The Saudi Arabian government encourages organizations to automate their routine activities to provide adequate services to their stakeholders. The performance of current Arabic QASs is limited to the specific domain. An effective QAS retrieves relevant responses from structured and unstructured data based on the user query. Many QAS studies categorized QASs according to factors, including user queries, dataset characteristics, and the nature of the responses. A more comprehensive examination of QASs is required to improve the QAS development according to the present QAS requirements. The current literature presents the features and classifications of the Arabic QAS. There is a lack of studies to report the techniques of Arabic QAS development. Thus, this study suggests a systematic literature review of strategies for developing Arabic QAS. A total of 617 articles were collected, and 40 papers were included in the proposed review. The outcome reveals the importance of the dataset and the deep learning techniques used to improve the performance of the QAS. The existing systems depend on supervised learning methods that lower QAS performance. In addition, the recent development of machine learning techniques encourages researchers to develop unsupervised QAS.

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Section: Resultsmentioning

confidence: 99%

“…Using an Arabic Morphological Analyzer, Nabil et al (2017) built an Arabic QAS. In addition, an explicit semantic approach was employed for ranking the pages.…”

Section: Resultsmentioning

confidence: 99%

A comprehensive survey of techniques for developing an Arabic question answering system

Alkhurayyif

Sait

2023

PeerJ Computer Science

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“…In the question-processing phase, the question is first classified in accordance with the temporal information it contains and then analyzed by removing stop words and extracting NEs. [52] proposed AlQuAnS, an Arabic language QA system that aims to provide a short Arabic answer from the World Wide Web for four types of factoid questions: number (date), location (country), location (city), and human (individual). AlQuAnS comprises two main parts: online and offline parts.…”

Section: Qa Evaluation Measuresmentioning

confidence: 99%

“…The maximum recall (100%) was reached by JAWEB[45], the maximum F1-measure (78.89%) was reached by AQuASys[40], and the maximum AQ measure (68.62%) was reached in[44]. The minimum accuracy, AQ, and MRR measures (22.2%, 47.66%, and 8.16%, respectively) were reached by AlQuAnS[52], which experimented with 200 open-domain QA pairs. The minimum precision reached 66.25% on AQuASys[40], which experimented with 80 QA pairs.…”

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confidence: 99%

Arabic Question Answering Systems: Gap Analysis

2021

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Question-answering (QA) systems aim to provide answers for given questions. The answers can be extracted or generated from either unstructured or structured text. Therefore, QA is considered an important field that can be used to evaluate machine text understanding. Arabic is a challenging language for many reasons; although it is spoken by more than 330 million native speakers, research on this language is limited. A few QA systems created for Arabic text are available. They were created to experiment on small datasets, some of which are unavailable. The research on QA systems can be expanded into different components of QA systems, such as question analysis, information retrieval, and answer extraction. The objective of this research is to analyze the QA systems created for Arabic text by reviewing, categorizing, and analyzing the gaps by providing advice to those who would like to work in this field. Six benchmark datasets are available for testing and evaluating Arabic QA systems, and 26 selected Arabic QA systems are analyzed and discussed in this research.

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“…In 2017 AlQuAnS [40] presented a system which combines different algorithms used in QAS to create a novel approach to the process of answer extraction. It used the Explicit Semantic Approach (ESA) in the passage retrieval process for passages ranking.…”

Section: B Question Answering System For Arabic Languagementioning

confidence: 99%

Leveraging Linked Open Data to Automatically Answer Arabic Questions

et al. 2019

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The interchangeably connected Web technologies and the advancements that accompany the semantic web content's leaps, have raised many challenges in the results' retrieval process especially for the Arabic Language. This research targets an important, yet insufficiently precedent, area in using Linked Open Data (LOD) for Automatic Question Answering systems in the Arabic Language. The significance of work presented, comes from its ability to overcome many challenges in querying Arabic content. Some of these challenges are: (a) bridging the gap between natural language and linked data by mapping users' queries to a standard semantic web query language such as SPARQL, (b) facilitating multilingual access to semantic data, and (c) maintaining the quality of data. Another challenging aspect was the lack of related work and publicly available resources for Arabic Question Answering Systems over Linked Data, despite the vastly growing Arabic corpus on the web. This paper presents a novel approach that targets Automatic Arabic Questions' Answering Systems whilst bypassing many featured challenges in the field. A hybrid approach that evaluates the effectiveness of using LOD to automatically answer Arabic questions is developed. The approach is developed to map users' questions in Modern Standard Arabic, to a standard query language for LOD (i.e. SPARQL) through: (i) extracting entities from questions and linking them over the web using Named-Entity Recognition and Disambiguation (NER/NED), and (ii) extracting properties among extracted named entities using a dependency parsing approach integrated with Wikidata ontology. To evaluate our proposed system, an Arabic questions dataset was created including: (a) Question body in Arabic language, (b) Question type, (c) SPARQL Query formulation, and (d) Question answer. Evaluation results are promising with a Precision of 84%, a Recall of 81.3%, and an F-Measure of 82.8%.

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AlQuAnS – An Arabic Language Question Answering System

Cited by 6 publications

References 7 publications

A comprehensive survey of techniques for developing an Arabic question answering system

A comprehensive survey of techniques for developing an Arabic question answering system

Arabic Question Answering Systems: Gap Analysis

Leveraging Linked Open Data to Automatically Answer Arabic Questions

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