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Syntax and Semantics Question Analysis Using User Modelling and Relevance Feedback

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@article{IJASEIT1334,
   author = {Syarilla I. Ahmad Saany and Ali Mamat and Aida Mustapha and Lilly S. Affendey and M. Nordin A. Rahman},
   title = {Syntax and Semantics Question Analysis Using User Modelling and Relevance Feedback},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {1},
   year = {2017},
   pages = {329--337},
   keywords = {syntax and semantic analysis; question analysis; question answering},
   abstract = {

A Question Answering (QA) system aims to provide relevant answers to users’ natural language (NL) questions by consulting its knowledge base (KB). Providing users with the most relevant answers to their questions is an issue. Many answers returned are not relevant to the questions and this issue is due to many factors. One such factor is the ambiguity yield during the semantic analysis of lexical extracted from the user’s question. The existing techniques did not consider some of the terms, called modifier terms, in the user’s question which are claimed to have a significant impact of returning correct answer. The objective of this study is to present the syntax and semantic question analysis using user modelling (UM) and relevance feedback (RF). This analysis interprets all the modifier terms in the user’s question in order to yield correct answers. A combination of UM and RF is used to increase the accuracy of the returned answer. UM helps the QA system to understand the user’s question and manage for question adjustment. RF provides an extended framework for the QA system to avoid or remedy the ambiguity of the user’s question. The analysis utilizes Vector Space Model (VSM) to semantically interpret and correctly converts modifier terms into a quantifiable form. The finding of this analysis demonstrates good precision percentage of 94.7% in returning relevant answers for each NL question.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1334},    doi = {10.18517/ijaseit.7.1.1334} }

EndNote

%A Ahmad Saany, Syarilla I.
%A Mamat, Ali
%A Mustapha, Aida
%A Affendey, Lilly S.
%A A. Rahman, M. Nordin
%D 2017
%T Syntax and Semantics Question Analysis Using User Modelling and Relevance Feedback
%B 2017
%9 syntax and semantic analysis; question analysis; question answering
%! Syntax and Semantics Question Analysis Using User Modelling and Relevance Feedback
%K syntax and semantic analysis; question analysis; question answering
%X 

A Question Answering (QA) system aims to provide relevant answers to users’ natural language (NL) questions by consulting its knowledge base (KB). Providing users with the most relevant answers to their questions is an issue. Many answers returned are not relevant to the questions and this issue is due to many factors. One such factor is the ambiguity yield during the semantic analysis of lexical extracted from the user’s question. The existing techniques did not consider some of the terms, called modifier terms, in the user’s question which are claimed to have a significant impact of returning correct answer. The objective of this study is to present the syntax and semantic question analysis using user modelling (UM) and relevance feedback (RF). This analysis interprets all the modifier terms in the user’s question in order to yield correct answers. A combination of UM and RF is used to increase the accuracy of the returned answer. UM helps the QA system to understand the user’s question and manage for question adjustment. RF provides an extended framework for the QA system to avoid or remedy the ambiguity of the user’s question. The analysis utilizes Vector Space Model (VSM) to semantically interpret and correctly converts modifier terms into a quantifiable form. The finding of this analysis demonstrates good precision percentage of 94.7% in returning relevant answers for each NL question.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1334 %R doi:10.18517/ijaseit.7.1.1334 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 1 %@ 2088-5334

IEEE

Syarilla I. Ahmad Saany,Ali Mamat,Aida Mustapha,Lilly S. Affendey and M. Nordin A. Rahman,"Syntax and Semantics Question Analysis Using User Modelling and Relevance Feedback," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 1, pp. 329-337, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.1.1334.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Ahmad Saany, Syarilla I.
AU  - Mamat, Ali
AU  - Mustapha, Aida
AU  - Affendey, Lilly S.
AU  - A. Rahman, M. Nordin
PY  - 2017
TI  - Syntax and Semantics Question Analysis Using User Modelling and Relevance Feedback
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 1
Y2  - 2017
SP  - 329
EP  - 337
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - syntax and semantic analysis; question analysis; question answering
N2  - 

A Question Answering (QA) system aims to provide relevant answers to users’ natural language (NL) questions by consulting its knowledge base (KB). Providing users with the most relevant answers to their questions is an issue. Many answers returned are not relevant to the questions and this issue is due to many factors. One such factor is the ambiguity yield during the semantic analysis of lexical extracted from the user’s question. The existing techniques did not consider some of the terms, called modifier terms, in the user’s question which are claimed to have a significant impact of returning correct answer. The objective of this study is to present the syntax and semantic question analysis using user modelling (UM) and relevance feedback (RF). This analysis interprets all the modifier terms in the user’s question in order to yield correct answers. A combination of UM and RF is used to increase the accuracy of the returned answer. UM helps the QA system to understand the user’s question and manage for question adjustment. RF provides an extended framework for the QA system to avoid or remedy the ambiguity of the user’s question. The analysis utilizes Vector Space Model (VSM) to semantically interpret and correctly converts modifier terms into a quantifiable form. The finding of this analysis demonstrates good precision percentage of 94.7% in returning relevant answers for each NL question.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1334 DO - 10.18517/ijaseit.7.1.1334

RefWorks

RT Journal Article
ID 1334
A1 Ahmad Saany, Syarilla I.
A1 Mamat, Ali
A1 Mustapha, Aida
A1 Affendey, Lilly S.
A1 A. Rahman, M. Nordin
T1 Syntax and Semantics Question Analysis Using User Modelling and Relevance Feedback
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 1
YR 2017
SP 329
OP 337
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 syntax and semantic analysis; question analysis; question answering
AB 

A Question Answering (QA) system aims to provide relevant answers to users’ natural language (NL) questions by consulting its knowledge base (KB). Providing users with the most relevant answers to their questions is an issue. Many answers returned are not relevant to the questions and this issue is due to many factors. One such factor is the ambiguity yield during the semantic analysis of lexical extracted from the user’s question. The existing techniques did not consider some of the terms, called modifier terms, in the user’s question which are claimed to have a significant impact of returning correct answer. The objective of this study is to present the syntax and semantic question analysis using user modelling (UM) and relevance feedback (RF). This analysis interprets all the modifier terms in the user’s question in order to yield correct answers. A combination of UM and RF is used to increase the accuracy of the returned answer. UM helps the QA system to understand the user’s question and manage for question adjustment. RF provides an extended framework for the QA system to avoid or remedy the ambiguity of the user’s question. The analysis utilizes Vector Space Model (VSM) to semantically interpret and correctly converts modifier terms into a quantifiable form. The finding of this analysis demonstrates good precision percentage of 94.7% in returning relevant answers for each NL question.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1334 DO - 10.18517/ijaseit.7.1.1334