# A logical representation of Arabic questions toward automatic passage extraction from the Web

Source: [A logical representation of Arabic questions toward automatic passage extraction from the Web](<https://www.sota2.com/research/paper/68fa64611e209131cea042af>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:35.934Z

Authors: Patrice Bellot, Wided Bakari, Mahmoud Neji

Published: 2018-05-23T13:21:40.000Z

## Abstract

With the expanding growth of Arabic electronic data on the web, extracting information, which is actually one of the major challenges of the question-answering, is essentially used for building corpus of documents. In fact, building a corpus is a research topic that is currently referred to among some other major themes of conferences, in Natural Language Processing (NLP), such as, Information Retrieval (IR), Question-Answering (QA), Automatic Summary (AS), etc. Generally, a question-answering system provides various passages to answer the user questions. To make these passages truly informative, this system needs access to an underlying knowledge base; this requires the construction of a corpus. The aim of our research is to build an Arabic question-answering system. In addition, analyzing the question must be the first step. Next, it is essential to retrieve a passage from the web that can serve as an appropriate answer. In this paper, we propose a method to analysis the question and retrieve the passage answer in the Arabic language. For the question analysis, five factual question types are processed. Additionally, our purpose is to experiment with the generation of a logic representation from the declarative form of each question. Several studies, deal with the logic approaches in question-answering, are discussed in other languages than the Arabic language. This representation is very promising because it helps us later in the selection of a justifiable answer. The accuracy of questions that are correctly analyzed and translated into the logic form achieved 64%. And then, the results of passages of texts that are automatically generated achieved an 87% score for accuracy and a 98% score for c@1.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1805.09120>)

[PDF](<https://arxiv.org/pdf/1805.09120.pdf>)