Browse wiki

Jump to: navigation, search
Open domain question answering using Wikipedia-based knowledge model
Abstract This paper describes the use of Wikipedia This paper describes the use of Wikipedia as a rich knowledge source for a question answering (QA) system. We suggest multiple answer matching modules based on different types of semi-structured knowledge sources of Wikipedia, including article content, infoboxes, article structure, category structure, and definitions. These semi-structured knowledge sources each have their unique strengths in finding answers for specific question types, such as infoboxes for factoid questions, category structure for list questions, and definitions for descriptive questions. The answers extracted from multiple modules are merged using an answer merging strategy that reflects the specialized nature of the answer matching modules. Through an experiment, our system showed promising results, with a precision of 87.1%, a recall of 52.7%, and an F-measure of 65.6%, all of which are much higher than the results of a simple text analysis based system. © 2014 Elsevier Ltd. All rights reserved. © 2014 Elsevier Ltd. All rights reserved.
Abstractsub This paper describes the use of Wikipedia This paper describes the use of Wikipedia as a rich knowledge source for a question answering (QA) system. We suggest multiple answer matching modules based on different types of semi-structured knowledge sources of Wikipedia, including article content, infoboxes, article structure, category structure, and definitions. These semi-structured knowledge sources each have their unique strengths in finding answers for specific question types, such as infoboxes for factoid questions, category structure for list questions, and definitions for descriptive questions. The answers extracted from multiple modules are merged using an answer merging strategy that reflects the specialized nature of the answer matching modules. Through an experiment, our system showed promising results, with a precision of 87.1%, a recall of 52.7%, and an F-measure of 65.6%, all of which are much higher than the results of a simple text analysis based system. © 2014 Elsevier Ltd. All rights reserved. © 2014 Elsevier Ltd. All rights reserved.
Bibtextype article  +
Doi 10.1016/j.ipm.2014.04.007  +
Has author Ryu P.-M. + , Jang M.-G. + , Kim H.-K. +
Has extra keyword Information management + , Factoid questions + , Knowledge sources + , Merging strategy + , Open domain question answering + , Question answering + , Question answering systems + , Semi-structured + , Wikipedia + , Data processing +
Has keyword Question-answering + , Semi-structured knowledge + , Wikipedia +
Issn 3064573  +
Issue 5  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 683–692  +
Published in Information Processing and Management +
Title Open domain question answering using Wikipedia-based knowledge model +
Type journal article  +
Volume 50  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 6 November 2014 16:03:23  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Journal articles  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 16:03:23  +
DateThis property is a special property in this wiki. 2014  +
hide properties that link here 
Open domain question answering using Wikipedia-based knowledge model + Title
 

 

Enter the name of the page to start browsing from.