<scp><i>Lemaza</i></scp>: An Arabic why-question answering system

Azmi, Aqil M.; AlShenaifi, Nouf

doi:10.1017/s1351324917000304

Cited by 25 publications

(16 citation statements)

References 46 publications

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“…The results showed that the RST-based method outperformed the baseline method. The same authors [83] experimented with their RST-based method by using 110 "why" questions on the same dataset. The test was to evaluate the performance of the RST-based method when one of the NLP techniques of the first and second components was skipped each time.…”

Section: Proposed Qa Systems For Arabic Nonfactoid Questionsmentioning

confidence: 99%

“…Content may change prior to final publication. [83] and FastQA [84]. These models can be used in the answer extraction component of QA systems.…”

Section: Proposed Qa Systems For Arabic Nonfactoid Questionsmentioning

confidence: 99%

“…LingPipe is a multilingual tool kit that is used for finding name entities, such as people names, location, or organization. The Protégé tool [83] is a domain-independent open-source tool used in creating ontologies and managing terminologies [84]. The NooJ linguistic engine [85] The number of proposed QA datasets for the Arabic language reached six; two of these datasets were created for nonfactoid questions, two for factoid questions, and the remaining two for hybrid questions, which are a combination of factoid and nonfactoid questions.…”

Section: E Systems and Toolsmentioning

confidence: 99%

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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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Section: Proposed Qa Systems For Arabic Nonfactoid Questionsmentioning

confidence: 99%

“…Content may change prior to final publication. [83] and FastQA [84]. These models can be used in the answer extraction component of QA systems.…”

Section: Proposed Qa Systems For Arabic Nonfactoid Questionsmentioning

confidence: 99%

Section: E Systems and Toolsmentioning

confidence: 99%

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Arabic Question Answering Systems: Gap Analysis

2021

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“…LEMAZA (Azmi et al, 2017) is an open-domain Arabic Question Answering system concerned with answering questions of the type "why". After the Question Analysis, and the text Document Preprocessing, phases, the most related passages to the question are extracted and verified in the Document|Passage Retrieval phase using Lemur retrieval engine.…”

Section: Related Workmentioning

confidence: 99%

“…The components of Question Answering systems differ according to their purpose. Some systems specialize in answering questions asked within a specific domain (such as Islamic questions) (Abdi et al, 2020), and some are concerned with answering questions of a specific type ("why", "when",…) (Azmi et al, 2017). Other systems, called open-domains Question Answering systems, try to answer many types of questions and within different domains, and they are the most general systems (AL-SMADI et al, 2017; Zhou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A new Passage Retrieval Method in Arabic Question Answering Systems

Alsabbagh¹,

Al-Dakkak²,

Ghneim³

2020

Preprint

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In this paper, we present our approach to improve the performance of open-domain Arabic Question Answering systems. We focus on the passage retrieval phase which aims to retrieve the most related passages to the correct answer. To extract passages that are related to the question, the system passes through three phases: Question Analysis, Document Retrieval and Passage Retrieval. We define the passage as the sentence that ends with a dot ".". In the Question Processing phase, we applied the traditional NLP steps of tokenization, stopwords and unrelated symbols removal, and replacing the question words with their stems. We also applied Query Expansion by adding synonyms to the question words. In the Document Retrieval phase, we used the Vector Space Model (VSM) with TF-IDF vectorizer and cosine similarity. For the Passage Retrieval phase, which is the core of our system, we measured the similarity between passages and the question by a combination of the BM25 ranker and Word Embedding approach. We tested our system on ACRD dataset, which contains 1395 questions in different domains, and the system was able to achieve correct results with a precision of 92.2% and recall of 79.9% in finding the top-3 related passages for the query.

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ARmed question answering system

Fehri

Dardour

Haddar

2022

Concurrency and Computation

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Question answering, retrieving an exact answer to a question posed in natural language, is an issue which has widely been studied in the open domain over the last decades. This, however, remains a real challenge in the medical domain as most existing systems only support a limited amount of question and answer types. The problem with proposed methods for Arabic language in the medical domain is that there is often a conflict between the extracted answer and user's requirements. This conflict is related to ambiguity. Nevertheless, the method we propose has successfully tackled this problem. Thus, in this article, we introduce ARmed, a system for automatically answering medical questions for Arabic language. ARmed consists of corpora study, pre-processing, question analysis, documents/passages retrieval, and answer extraction. Compared with the previous studies, ARmed has the potential to handle a large number of questions and answer types. The experimental results show that ARmed achieves interesting results.

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Lemaza: An Arabic why-question answering system

Cited by 25 publications

References 46 publications

Arabic Question Answering Systems: Gap Analysis

Arabic Question Answering Systems: Gap Analysis

A new Passage Retrieval Method in Arabic Question Answering Systems

ARmed question answering system

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