Arabic Question Answering System for Information Retrieval on Large-scale Image Objects

2021 ◽  
Author(s):  
Sawsan Al-Zubi ◽  
Feras M. Awaysheh ◽  
Bashar Awad Al-Shboul
Keyword(s):  
Information Retrieval ◽  
Large Scale ◽  
Question Answering ◽  
Question Answering System ◽  
Image Objects

CONSTRUCTING AN ONTOLOGY-BASED AND GRAPH-BASED KNOWLEDGE REPRESENTATION OF ENGLISH QURAN

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Mohamad Fauzan Noordin ◽  
Tengku Mohd. Tengku Sembok ◽  
Roslina Othman ◽  
Ria Hari Gusmita
Keyword(s):  
Information Retrieval ◽  
Knowledge Representation ◽  
Question Answering ◽  
Graph Database ◽  
Retrieval Process ◽  
Valuable Contribution ◽  
Question Answering System ◽  
Part Of Speech

This paper describes a work in constructing two models of knowledge representation (KR) in aiming to do evaluation of their achievement in contributing to increase performance of retrieving information on English Quran domain. Due to many approaches available to construct a KR in providing data for information retrieval process, there is a need to find out in what model the KR could provide a valuable contribution for retrieving information. We focused on ontology-based KR and graph database-based KR. We use Quranic Arabic corpus that available at http://www.corpus.quran.com as a source to build the KR. We extracted several data from it i.e. English token, token location, and token Part of Speech (POS). Protégé is used to construct the ontology and Neo4j is utilized in developing the graph database. Both KR models will be equipped in developing of an English Quran Question Answering system in order to evaluate their benefit.

Research and Implementation of Intelligent Question Answering System in MOOC

2014 ◽  
Vol 678 ◽  
pp. 639-643
Author(s):  
Wei Jun Dong ◽  
Guo Hua Geng
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Open Educational Resource ◽  
Agent Model ◽  
Question Answering System ◽  
Passive Learning ◽  
Self Development ◽  
The Status ◽  
Massive Online Open Course

Massive Online Open Course which based on Open Educational Resource might be the most effective method to large-scale quality education, which can realize passive learning to active learning. Analyzing the status and shortages of Intelligent Answering System, propose and design an intelligent question answering system based on agent-model. System use software agents to implement and improve MOOC system’s Intelligent Answering System performance, which has capacity of natural language processing, and good versatility. It can provide an efficient online problem answer environment for thousands of learners, and can effectively promote students' autonomous learning and self-development.

A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base

2017 ◽  
Vol 11 (03) ◽  
pp. 345-371
Author(s):  
Avani Chandurkar ◽  
Ajay Bansal
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Knowledge Base ◽  
Language Processing ◽  
Question Answering ◽  
Automated System ◽  
Free Form ◽  
Question Answering System ◽  
Novel Approach

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

scholarly journals Question Answering System for Applicant Support by Using Modern Messengers

2018 ◽  
Vol 25 (4) ◽  
pp. 411-420
Author(s):  
Dmitry R. Filonov ◽  
Dmitry Ju. Chalyy ◽  
Dmitry M. Murin ◽  
Valery G. Durnev ◽  
Valery A. Sokolov
Keyword(s):  
Information Retrieval ◽  
Question Answering ◽  
Business Processes ◽  
Instant Messaging ◽  
Personal Data ◽  
Cloud Services ◽  
Question Answering System ◽  
The University ◽  
Formal Requirements ◽  
Future Work

There is an increasing interest to the instant messaging applications, messengers. These applications allow us to interact with other users and include a functionality that can help us to implement bots that automate various business processes or provide information services. In this paper, we consider a specialized question answering system that uses today’s messaging services infrastructure to support university applicants. We gathered a corpus of applicants questions throughout two years and developed an information retrieval model that helps us to find similar questions in the corpus. Applicants can type their questions using a natural language without any formal requirements to phrase construction or using special templates. If the system is unable to find a relevant answer, the user can directly address the question to representatives of the university. The system was implemented with the use of modern cloud services that are provided by Amazon. We used serverless computations and NoSQL data bases, so we had to develop an architecture of the system in that way. Since the system contains sensitive personal data and provide personalized service, we must focus our attention on security. We proposed the means that must improve the safety of the system, more specifically, authentification process that can be used without the explicit use of personal data, however, this is a future work. At present we test our system and evaluate its quality of information retrieval.

scholarly journals Penerapan Question Answering System Pada Pembahasan Agama Islam Dengan Pendekatan Metode Pattern Based

2018 ◽  
Vol 2 (4) ◽  
pp. 140 ◽  
Author(s):  
Ramadhana Rosyadi ◽  
Said Al-Faraby ◽  
Adiwijaya Adiwijaya
Keyword(s):  
Information Retrieval ◽  
Question Answering ◽  
Human Life ◽  
Specific Question ◽  
Question Answering System ◽  
Retrieval Systems ◽  
Question And Answer ◽  
Information Retrieval Systems ◽  
System Information ◽  
Selection Of

Islam has 25 prophets as guidelines for human life, documents containing information about the stories of the lives of the prophets during their lifetime. This study aims to build a more specific question and answer system by generating relevant answers not in the form of documents. Question Answering System is able to overcome problems in the Question and answer system, information retrieval systems where the answers issued are correct with responses to requests submitted, not in the form of documents that may contain answers. This study uses the Pattern Based method as extracting sentence pieces which are the answers to find answers that match the patterns that have been made. The selection of datasets causes a number of questions that can be submitted to be limited to information stored in the data itself. Besides that, questions are also limited in the form of Question words that are Factoid, namely Who, when, where, what and how. Accuracy results obtained using the Pattern Based method on Question Answering System are 39.36%.

Research and Implementation of Intelligent Question Answering System in MOOC

2014 ◽  
Vol 678 ◽  
pp. 684-688
Author(s):  
Wei Jun Dong ◽  
Guo Hua Geng
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
Question Answering ◽  
Open Educational Resource ◽  
Agent Model ◽  
Question Answering System ◽  
Passive Learning ◽  
Self Development ◽  
The Status ◽  
Massive Online Open Course

Massive Online Open Course which based on Open Educational Resource might be the most effective method to large-scale quality education, which can realize passive learning to active learning. Analyzing the status and shortages of Intelligent Answering System, propose and design an intelligent question answering system based on agent-model. System use software agents to implement and improve MOOC system’s Intelligent Answering System performance, which has capacity of natural language processing, and good versatility. It can provide an efficient online problem answer environment for thousands of learners, and can effectively promote students' autonomous learning and self-development.

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