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Keyphrase Extraction and Grouping Based on Association Rules

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dc.contributor.advisor Song, Fei
dc.contributor.author Li, Xin
dc.date.accessioned 2014-01-09T17:58:55Z
dc.date.available 2014-01-09T17:58:55Z
dc.date.copyright 2013-12
dc.date.created 2013-12-20
dc.date.issued 2014-01-09
dc.identifier.uri http://hdl.handle.net/10214/7787
dc.description.abstract Keyphrases are important in capturing the content of a document and thus useful for text representation. Keyphrase extraction is often needed for many natural language processing tasks such as Information Retrieval, Document Classification, and Text Summarization. It aims to extract multi-word terms from a collection of documents that more or less correspond to keyphrases. In this thesis, we propose a new method for keyphrase extraction based on association rule mining. Redundant multi-word terms or synonymous terms inevitably make up a big part of keyphrases extracted. With association rules, we can reduce the redundancy by grouping the related keyphrases that have strong co-occurrence frequencies. We further apply our keyphrase extraction and grouping methods to Information Retrieval. By both distinguishing and group- ing keyphrases, we are able to achieve improved performance for Information Retrieval. en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.subject Association Rules en_US
dc.subject Information Retrieval en_US
dc.subject Keyphrase Grouping en_US
dc.subject Keyphrase Extraction en_US
dc.subject Natural Language Processing en_US
dc.title Keyphrase Extraction and Grouping Based on Association Rules en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Computer Science en_US
dc.rights.license All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.degree.grantor University of Guelph en_US


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