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Syntax and Data-to-Text Generation Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Exploring Multidimensional Continuous Feature Space to Extract Relevant Words

Authors : Márius Šajgalík, Michal Barla, Mária Bieliková

Published in: Statistical Language and Speech Processing

Publisher: Springer International Publishing

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Abstract

With growing amounts of text data the descriptive metadata become more crucial in efficient processing of it. One kind of such metadata are keywords, which we can encounter e.g. in everyday browsing of webpages. Such metadata can be of benefit in various scenarios, such as web search or content-based recommendation. We research keyword extraction problem from the perspective of vector space and present a novel method to extract relevant words from an article, where we represent each word and phrase of the article as a vector of its latent features. We evaluate our method within text categorisation problem using a well-known 20-newsgroups dataset and achieve state-of-the-art results.

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previous chapter Supervised Classification Using Balanced Training
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Footnotes
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Metadata
Title
Exploring Multidimensional Continuous Feature Space to Extract Relevant Words
Authors
Márius Šajgalík
Michal Barla
Mária Bieliková
Copyright Year
2014
Book
Statistical Language and Speech Processing

Print ISBN: 978-3-319-11396-8

Electronic ISBN: 978-3-319-11397-5

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-11397-5_12

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