Question Expansion in a Question-Answering System in a Closed-Domain System

Since early days Question Answering (QA) has been an intuitive way of understanding the concept by humans. Considering its inevitable importance it has been introduced to children from very early age and they are promoted to ask more and more questions. With the progress in Machine Learning & Ontological semantics, Natural Language Question Answering (NLQA) has gained more popularity in recent years. In this paper QUASE (QUestion Answering System for Education) question answering system for answering natural language questions has been proposed which help to find answer for any given question in a closed domain containing finite set of documents. Th e QA s y st em m a inl y focuses on factoid questions. QUASE has used Question Taxonomy for Question Classification. Several Natural Language Processing techniques like Part of Speech (POS) tagging, Lemmatization, Sentence Tokenization have been applied for document processing to make search better and faster. DBPedia ontology has been used to validate the candidate answers. By application of this system the learners can gain knowledge on their own by getting precise answers to their questions asked in natural language instead of getting back merely a list of documents. The precision, recall and F measure metrics have been taken into account to evaluate the performance of answer type evaluation. The metric Mean Reciprocal Rank has been considered to evaluate the performance of QA system. Our experiment has shown significant improvement in classifying the questions in to correct answer types over other methods with approximately 91% accuracy and also providing better performance as a QA system in closed domain search.

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AQUA: A Closed-Domain Question Answering System

Information Systems Management ◽

10.1080/10580530.2010.493825 ◽

2010 ◽

Vol 27 (3) ◽

pp. 217-225 ◽

Cited By ~ 7

Author(s):

Maria Vargas-Vera ◽

Miltiadis D. Lytras

Keyword(s):

Question Answering ◽

Closed Domain ◽

Question Answering System

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Case based Indonesian closed domain question answering system with real world questions

2012 7th International Conference on Telecommunication Systems, Services, and Applications (TSSA) ◽

10.1109/tssa.2012.6366047 ◽

2012 ◽

Cited By ~ 2

Author(s):

Abdurrisyad Fikri ◽

Ayu Purwarianti

Keyword(s):

Real World ◽

Question Answering ◽

Closed Domain ◽

Question Answering System ◽

Case Based

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Semantics-Enhanced Answer Selection in Closed-domain Question Answering System

2018 Internat2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC)ional conference on computation of power, energy, Information and Communication (ICCPEIC) ◽

10.1109/iccpeic.2018.8525181 ◽

2018 ◽

Author(s):

M. Monisha Devi ◽

G K Venkateshwaran ◽

A Vignesh ◽

K Hariharan

Keyword(s):

Question Answering ◽

Closed Domain ◽

Question Answering System

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Eve: An Automated Question Answering System for Events Information

ComTech Computer Mathematics and Engineering Applications ◽

10.21512/comtech.v8i1.3781 ◽

2017 ◽

Vol 8 (1) ◽

pp. 15

Author(s):

Ivan Christanno ◽

Priscilla Priscilla ◽

Jody Johansyah Maulana ◽

Derwin Suhartono ◽

Rini Wongso

Keyword(s):

Question Answering ◽

Named Entity Recognition ◽

Entity Recognition ◽

Closed Domain ◽

Word Similarity ◽

Question Answering System ◽

Named Entity ◽

Document Indexing ◽

Matching Technique ◽

Processing Information

The objective of this research was to create a closed-domain of automated question answering system specifically for events called Eve. Automated Question Answering System (QAS) is a system that accepts question input in the form of natural language. The question will be processed through modules to finally return the most appropriate answer to the corresponding question instead of returning a full document as an output. Thescope of the events was those which were organized by Students Association of Computer Science (HIMTI) in Bina Nusantara University. It consisted of 3 main modules namely query processing, information retrieval, and information extraction. Meanwhile, the approaches used in this system included question classification, document indexing, named entity recognition and others. For the results, the system can answer 63 questions for word matching technique, and 32 questions for word similarity technique out of 94 questions correctly.

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