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Automatic Multi-word Term Extraction and its Application to Web-page Summarization

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dc.contributor.advisor Song, Fei
dc.contributor.author Huo, Weiwei
dc.date.accessioned 2012-12-20T21:43:19Z
dc.date.available 2012-12-20T21:43:19Z
dc.date.copyright 2012-12
dc.date.created 2012-11-27
dc.date.issued 2012-12-20
dc.identifier.uri http://hdl.handle.net/10214/4959
dc.description.abstract In this thesis we propose three new word association measures for multi-word term extraction. We combine these association measures with LocalMaxs algorithm in our extraction model and compare the results of different multi-word term extraction methods. Our approach is language and domain independent and requires no training data. It can be applied to such tasks as text summarization, information retrieval, and document classification. We further explore the potential of using multi-word terms as an effective representation for general web-page summarization. We extract multi-word terms from human written summaries in a large collection of web-pages, and generate the summaries by aligning document words with these multi-word terms. Our system applies machine translation technology to learn the aligning process from a training set and focuses on selecting high quality multi-word terms from human written summaries to generate suitable results for web-page summarization. en_US
dc.language.iso en en_US
dc.rights.uri http://creativecommons.org/licenses/by/2.5/ca/ *
dc.subject Multi-word Term Extraction en_US
dc.subject Generic Web-page Summarization en_US
dc.title Automatic Multi-word Term Extraction and its Application to Web-page Summarization 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
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