Text mining Wikipedia to discover alternative destinations

This paper discusses an application of some statistical Natural Language Processing algorithms to a set of articles from Wikipedia about top tourist destinations. The objective is to automatically identify the key features of each destination and then discover other destinations which share similar...

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Main Author: Kenneth Cosh
Format: Conference Proceeding
Published: 2018
Subjects:
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84883394861&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/52430
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Institution: Chiang Mai University
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spelling th-cmuir.6653943832-524302018-09-04T09:25:15Z Text mining Wikipedia to discover alternative destinations Kenneth Cosh Computer Science This paper discusses an application of some statistical Natural Language Processing algorithms to a set of articles from Wikipedia about top tourist destinations. The objective is to automatically identify the key features of each destination and then discover other destinations which share similar sets of features. Through this a method is demonstrated by which meta data about each article can be extracted from the unstructured text and then used to answer complex discovery type queries. The paper compares an approach to automatically clustering similar destinations with a more user driven feature focused technique. © 2013 IEEE. 2018-09-04T09:25:15Z 2018-09-04T09:25:15Z 2013-09-09 Conference Proceeding 2-s2.0-84883394861 10.1109/JCSSE.2013.6567317 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84883394861&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/52430
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Computer Science
spellingShingle Computer Science
Kenneth Cosh
Text mining Wikipedia to discover alternative destinations
description This paper discusses an application of some statistical Natural Language Processing algorithms to a set of articles from Wikipedia about top tourist destinations. The objective is to automatically identify the key features of each destination and then discover other destinations which share similar sets of features. Through this a method is demonstrated by which meta data about each article can be extracted from the unstructured text and then used to answer complex discovery type queries. The paper compares an approach to automatically clustering similar destinations with a more user driven feature focused technique. © 2013 IEEE.
format Conference Proceeding
author Kenneth Cosh
author_facet Kenneth Cosh
author_sort Kenneth Cosh
title Text mining Wikipedia to discover alternative destinations
title_short Text mining Wikipedia to discover alternative destinations
title_full Text mining Wikipedia to discover alternative destinations
title_fullStr Text mining Wikipedia to discover alternative destinations
title_full_unstemmed Text mining Wikipedia to discover alternative destinations
title_sort text mining wikipedia to discover alternative destinations
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84883394861&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/52430
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