Abstract
This paper presents a TV content augmentation system that enhances the contents of TV programs by retrieving context related data and presenting them to the viewers without the necessity of another device. The paper presents both the conceptual description of the system and a prototype implementation. The implementation utilizes program descriptions crawled from web resources in order to extract named entities such as person names, locations, organizations, etc. For this purpose, a rule based Named Entity Recognition (NER) algorithm is developed for Turkish texts. Information about the extracted entities is retrieved from Wikipedia with the help of semantic disambiguation and its summarized form is presented to the users. A set of experiments have been conducted on two different data sets in order to evaluate the performance of the rule based NER algorithm and the behavior of the TV content augmentation system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Taylor, A., Harper, R.: Analysis of Routine TV Watching Hbits and Their İmplications for Electronic Program Guide Design, pp. 1–12 (2002)
Dimitrova, N., Zimmerman, J., Janevski, A., Agnihotri, L., Haas, N., Bolle, R.: Content augmentation aspects of personalized entertainment experience. In: Proceedings of Third workshop on Personalization in Future TV (2003)
Chattopadhyay, T., Pal, A., Garain, U.: Mash up of breaking news and contextual web information: a novel service for connected television. In: Proceedings of International Conference on Computer and Communication Networks, ICCCN (2010)
Martin R., Holtzman, H.: Newstream: A Multi-Device, Cross-Medium, and Socially Aware Approach to News Content, pp. 83–90. (2010)
Hemsley, R., Ducao, A., Toledano, E., Holtzman, H.: ContextController: Augmenting broadcast TV with Real-Time Contextual Information, pp. 833–836 (2013)
Prata, A., Chambel, T.: Going Beyond iTV: designing flexible video-based crossmedia interactive services as informal learning contexts. In: Proceedings of EuroITV’11—Ubiquitous TV Conference, pp. 65–74 (2011)
Dilek, K.: Named Entity Recognition Experiments on Turkish Texts, pp. 524–535 (2009)
Küçük, D., Yazıcı, A.: A hybrid named entity recognizer for Turkish. Expert Syst. Appl. 39(3), 2733–2742 (2012)
Freitag, D.: Machine learning for information extraction in informal domains. Mach. Learn. 39, 169–202 (2000)
Tatar, S., Cicekli, I.: Automatic rule learning exploiting morphological features for named entity recognition in Turkish. J. Inf. Sci. 37(2), 137–151 (2011)
Akın, A.A., Akın, M.D.: Zemberek, an open source nlp framework for Turkic languages. Structure (2007). http://zemberek.googlecode.com/
Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval, vol. 9. ACM press, New York (1999)
Acknowledgments
This work is supported by the Scientific and Technical Council of Turkey Grant TUBITAK EEEAG-112E111
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Işıklar, Y.E., Çiçekli, N. (2016). A TV Content Augmentation System Exploiting Rule Based Named Entity Recognition Method. In: Abdelrahman, O., Gelenbe, E., Gorbil, G., Lent, R. (eds) Information Sciences and Systems 2015. Lecture Notes in Electrical Engineering, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-319-22635-4_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-22635-4_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22634-7
Online ISBN: 978-3-319-22635-4
eBook Packages: EngineeringEngineering (R0)