Arabic Question Answering: A Study on Challenges, Systems, and Techniques
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A review of the Arabic Question Answering Systems building processes and the challenges met by the researchers in this topic due to the Arabic language special characteristics are provided.Abstract:
The enormous increase of the amount of information available on the web creates the need for systems like Question Answering to bridge the gap between general end users and the web with its different data representations. A considerable portion of the available data on the web is written in Arabic for and by Arabic users. This paper provides a review of the Arabic Question Answering Systems building processes and the challenges met by the researchers in this topic due to the Arabic language special characteristics. A general architecture is represented for the Question Answering task on both structured and unstructured data. Then, an overview of the work done in Arabic Question Answering Systems is presented. Finally, a number of tools and linguistic resources are recommended for researchers to develop Arabic question answering systems. General Terms Question Answering, Question Answering Systems, Natural Language Processing, Information Retrievalread more
Citations
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Arabic question answering system: a survey
TL;DR: The challenges due to the language and how these challenges make the development of new Arabic QAS more difficult are discussed, followed by an in-depth analysis of the techniques and approaches in the three modules of a QAS.
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Recent Developments in Arabic Conversational AI: A Literature Review
TL;DR: This work provides a thorough review of recent Arabic conversational AI systems and group them into three categories based on their functionality: question-answering (QA) systems, task-oriented dialogue systems (DS), and chatbots.
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Recent Developments in Arabic Conversational AI: A Literature Review
TL;DR: In this paper , the authors provide a thorough review of recent Arabic conversational AI systems and group them into three categories based on their functionality: (1) question-answering (QA), (2) task-oriented dialogue systems (DS), and (3) chatbots.
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Arabic factoid Question-Answering system for Islamic sciences using normalized corpora.
TL;DR: An Arabic QA system for factoid questions specialized in Islamic sciences as prophetic tradition (Hadith), Hadith narrator and Quran interpretation (Tafsir) and a method composed of three phases to retrieve an accurate answer for the user question is proposed.
SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs
TL;DR: In this paper, SERAG (Semantic Entity Retrieval from Arabic knowledge graphs) uses random walks to generate entity embeddings for non-English languages in general and Arabic in particular.
References
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Proceedings ArticleDOI
Information Extraction over Structured Data: Question Answering with Freebase
Xuchen Yao,Benjamin Van Durme +1 more
TL;DR: It is shown that relatively modest information extraction techniques, when paired with a webscale corpus, can outperform these sophisticated approaches by roughly 34% relative gain.
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A survey of arabic named entity recognition and classification
TL;DR: The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated.
Proceedings ArticleDOI
Probabilistic question answering on the web
TL;DR: The architecture that augments existing search engines so that they support natural language question answering, called NSIR, is developed and some probabilistic approaches to the last three of these stages are described.
Journal ArticleDOI
A Survey of Text Question Answering Techniques
Poonam Gupta,Vishal Gupta +1 more
TL;DR: Question Answering (QA) systems give the ability to answer questions posed in natural language by extracting, from a repository of documents, fragments of documents that contain material relevant to the answer.
Book ChapterDOI
An Introduction to Question Answering over Linked Data
TL;DR: This tutorial gives an introduction to the rapidly developing field of question answering over linked data and gives an overview of the main challenges involved in the interpretation of a user’s information need expressed in natural language with respect to the data that is queried.