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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| Format: | Conference Proceeding |
| Published: |
2018
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| 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 |
| Summary: | 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. |
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