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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Notes
- 1.
Available online at - https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing (last accessed on May 14, 2014).
- 2.
Available online at - http://www.washingtonpost.com/world/national-security/cybersecurity-poll-americans-divided-over-government-requirements-on-companies/2012/06/06/gJQAmWqnJV_story.html (last accessed on May 14, 2014).
- 3.
Available online at - http://storage.googleapis.com/books/ngrams/books/datasetsv2.html (last accessed on May 14, 2014).
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Acknowledgement
This work was partially supported by grants No. VG1/0675/11, APVV-0208-10 and it is the partial result of the Research and Development Operational Programme project “University Science Park of STU Bratislava”, ITMS 26240220084, co-funded by the European Regional Development Fund.
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Šajgalík, M., Barla, M., Bieliková, M. (2014). Exploring Multidimensional Continuous Feature Space to Extract Relevant Words. In: Besacier, L., Dediu, AH., Martín-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2014. Lecture Notes in Computer Science(), vol 8791. Springer, Cham. https://doi.org/10.1007/978-3-319-11397-5_12
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DOI: https://doi.org/10.1007/978-3-319-11397-5_12
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