QArabPro: A Rule Based Question Answering System for Reading Comprehension Tests in Arabic

Akour, Mohammed; Abufardeh, Sameer; Magel, Kenneth; Al-Radaideh, Qasem A.

doi:10.3844/ajassp.2011.652.661

Cited by 34 publications

(25 citation statements)

References 15 publications

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“…This corpus is available free online [22] and to be able to compare proposed system with similar systems where the system was tested by different people on different question type like (Who Ϧϣ, Where Ϧϳ, What Ύϣ). By using ANERcorp the proposed system gives a more enhanced results than the other systems as shown in Table 2 the recall is up to 96.6% which shows a better result than similar systems Answerbus [23], QArabPro (Arabic) [24] and ArQA [25]. Figure 4.…”

Section: Answer Ranking Phasementioning

confidence: 87%

Enhancing Arabic Question Answering System

Kamal¹,

Azim²

2014

2014 International Conference on Computational Intelligence and Communication Networks

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During the last few decades many Arabic question answering systems have been developed. These systems may extract answers from texts or web-pages. None of these systems make use of question answering database where user can present questions in natural language which differ from the stored questions. The proposed system uses information retrieval approaches to get to the closest answers to the input question, so the system gives partially or totally correct answers. The Latent Semantic Indexing (LSI) is implemented to enhance the resultant selections. Arabic natural language processing is used in the proposed system along with LSI.Keywords-question answering system; natural language processing; information retrieval; latent semantic indexing I.

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Section: Answer Ranking Phasementioning

confidence: 87%

Enhancing Arabic Question Answering System

Kamal¹,

Azim²

2014

2014 International Conference on Computational Intelligence and Communication Networks

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“…Akour et al (2011) designed QArabPro, which is a rule-based system for reading comprehension tests. Although the system tackled any type of question, the dataset was limited to only seventy-five questions.…”

Section: Related Workmentioning

confidence: 99%

Arabic community question answering

Nakov¹,

Màrquez²,

Moschitti³

et al. 2018

Nat. Lang. Eng.

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We analyze resources and models for Arabic community Question Answering (cQA). In particular, we focus on CQA-MD, our cQA corpus for Arabic in the domain of medical forums. We describe the corpus and the main challenges it poses due to its mix of informal and formal language, and of different Arabic dialects, as well as due to its medical nature. We further present a shared task on cQA at SemEval, the International Workshop on Semantic Evaluation, based on this corpus. We discuss the features and the machine learning approaches used by the teams who participated in the task, with focus on the models that exploit syntactic information using convolutional tree kernels and neural word embeddings. We further analyze and extend the outcome of the SemEval challenge by training a meta-classifier combining the output of several systems. This allows us to compare different features and different learning algorithms in an indirect way. Finally, we analyze the most frequent errors common to all approaches, categorizing them into prototypical cases, and zooming into the way syntactic information in tree kernel approaches can help solve some of the most difficult cases. We believe that our analysis and the lessons learned from the process of corpus creation as well as from the shared task analysis will be helpful for future research on Arabic cQA.

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“…The authors selected as a baseline the output of the answer retrieval model with BM25 similarity formula (ranking function based on binary independence model); their system achieved a 14% improvement in MRR over their baseline. Akour et al (2011) introduced the QArabPro system for the Arabic language based on a set of separate rules for each type of question. The authors used the same method to handle all question types including "why" questions.…”

Section: Related Workmentioning

confidence: 99%

A Discourse-Based Approach for Arabic Question Answering

Sadek

Meziane

2016

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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A di s c o u r s e-b a s e d a p p r o a c h fo r Ar a bi c q u e s tio n a n s w e ri n g S a d e k, J a n d M e zi a n e, F h t t p:// dx. d oi.o r g/ 1 0. 1 1 4 5/ 2 9 8 8 2 3 8Ti t l e A di s c o u r s e-b a s e d a p p r o a c h fo r Ar a bi c q u e s tio n a n s w e ri n g A u t h o r s S a d e k, J a n d M e zi a n e , F Typ e Articl e U RL This ve r sio n is a v ail a bl e a t : h t t p:// u sir.s alfo r d. a c. u k/id/ e p ri n t/ 3 8 1 2 2/ P u b l i s h e d D a t e 2 0 1 6 U SIR is a di git al c oll e c tio n of t h e r e s e a r c h o u t p u t of t h e U niv e r si ty of S alfo r d. W h e r e c o py ri g h t p e r mi t s, full t e x t m a t e ri al h el d in t h e r e p o si to ry is m a d e fr e ely a v ail a bl e o nli n e a n d c a n b e r e a d , d o w nlo a d e d a n d c o pi e d fo r n o nc o m m e r ci al p riv a t e s t u dy o r r e s e a r c h p u r p o s e s . Pl e a s e c h e c k t h e m a n u s c ri p t fo r a n y fu r t h e r c o py ri g h t r e s t ri c tio n s. Fo r m o r e info r m a tio n, in cl u di n g o u r p olicy a n d s u b mi s sio n p r o c e d u r e , pl e a s e c o n t a c t t h e R e p o si to ry Te a m a t: u si r@ s alfo r d. a c. u k .The treatment of complex questions with explanatory answers involves searching for arguments in texts. Because of the prominent role that discourse relations play in reflecting text-producers' intentions, capturing the underlying structure of text constitutes a good instructor in this issue. From our extensive review, a system for automatic discourse analysis that creates full rhetorical structures in large scale Arabic texts is currently unavailable. This is due to the high computational complexity involved in processing a large number of hypothesized relations associated with large texts. Therefore, more practical approaches should be investigated. This paper presents a new Arabic Text Parser oriented for question answering systems dealing with ‫ﻟﻤﺎذا‬ "why" and ‫"ﻛﯿﻒ‬how to" questions. The Text Parser presented here considers the sentence as the basic unit of text and incorporates a set of heuristics to avoid computational explosion. With this approach, the developed question answering system reached a significant improvement over the baseline with a Recall of 68% and MRR of 0.62.

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QArabPro: A Rule Based Question Answering System for Reading Comprehension Tests in Arabic

Cited by 34 publications

References 15 publications

Enhancing Arabic Question Answering System

Enhancing Arabic Question Answering System

Arabic community question answering

A Discourse-Based Approach for Arabic Question Answering

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