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A TV Content Augmentation System Exploiting Rule Based Named Entity Recognition Method

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 363)

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.

Keywords

Content augmentation Connected TV EPG (Electronic Program Guide) Named Entity Recognition (NER) Semantic disambiguation 

Notes

Acknowledgments

This work is supported by the Scientific and Technical Council of Turkey Grant TUBITAK EEEAG-112E111

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Dept. of Computer Engineering METUAnkaraTurkey

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