Abstract
Keyphrase extraction is to select the most representative phrases within a given text. While supervised methods require a large amount of training data, unsupervised methods can perform without prior knowledge such as language. In this paper, we propose a ranking algorithm based on unsupervised keyphrase extraction and develop a framework for retrieving opinion articles. Since the proposed algorithm uses an unsupervised method, it can be employed to multi-language systems. Moreover, our proposed ranking algorithm measures the importance in three aspects, the amount of information within articles, representativeness of sentences, and frequency of words. Our framework shows better performance than previous algorithms in terms of precision and NDCG.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aizawa AN (2003) An information-theoretic perspective of Tf-idf measures. J Info Process Manage 39(1):45–65
Eirinaki M, Pisal S, Singh J (2012) Feature-based opinion mining and ranking. J Comp Syst Sci 78(4):1175–1184
Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632
Mihalcea R, Tarau P (2004) TextRank: bringing order into text. In: Proceedings of EMLNP 2004, Barcelona, pp 404–411
Page L, Brin S, Motwani R, Winograd T (1999) The PageRank citation ranking: bringing order to the Web. Technical report, Stanford InfoLab
Yun U, Ryang H, Pyun G, Lee G (2012) Efficient opinion article retrieval system. Lecture Note in Computer Science. In: Proceedings of ICHIT 2012, Daejeon. pp 566–573
Zhang L, Liu B, Lim SH, O’Brien-Strain E (2010) Extracting and ranking product features in opinion documents. In: Proceedings of COLING 2010, Beijing, pp 1462–1470
Acknowledgements
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2012-0003740 and 2012-0000478).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht(Outside the USA)
About this paper
Cite this paper
Ryang, H., Yun, U. (2013). Unsupervised Keyphrase Extraction Based Ranking Algorithm for Opinion Articles. In: Park, J., Ng, JY., Jeong, HY., Waluyo, B. (eds) Multimedia and Ubiquitous Engineering. Lecture Notes in Electrical Engineering, vol 240. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6738-6_14
Download citation
DOI: https://doi.org/10.1007/978-94-007-6738-6_14
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-6737-9
Online ISBN: 978-94-007-6738-6
eBook Packages: EngineeringEngineering (R0)