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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 Huo, Weiwei 2012-12-20T21:43:19Z 2012-12-20T21:43:19Z 2012-12 2012-11-27 2012-12-20
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 *
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 Computer Science en_US Master of Science en_US School of Computer Science en_US
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