News auto-tagging using Wikipedia

Author

Shams Eldin, Shaimaa ; El-Beltagy, S.R.

Author_Institution

Center for Inf. Sci., Nile Univ., Giza, Egypt

fYear

2013

fDate

17-19 March 2013

Firstpage

158

Lastpage

163

Abstract

This paper presents an efficient method for automatically annotating Arabic news stories with tags using Wikipedia. The idea of the system is to use Wikipedia article names, properties, and re-directs to build a pool of meaningful tags. Sophisticated and efficient matching methods are then used to detect text fragments in input news stories that correspond to entries in the constructed tag pool. Generated tags represent real life entities or concepts such as the names of popular places, known organizations, celebrities, etc. These tags can be used indirectly by a news site for indexing, clustering, classification, statistics generation or directly to give a news reader an overview of news story contents. Evaluation of the system has shown that the tags it generates are better than those generated by MSN Arabic news.

Keywords

Web sites; indexing; natural language processing; pattern clustering; pattern matching; text analysis; MSN Arabic news; Wikipedia article names; Wikipedia article properties; automatic Arabic news story annotation; input news story text fragment detection; matching methods; news auto-tagging; news site classification; news site clustering; news site indexing; news story contents; real life entities; statistics generation; Dictionaries; Electronic publishing; Encyclopedias; Indexing; Internet; Tagging; Arabic text; Disambiguation; Tagging; Wikipedia;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Information Technology (IIT), 2013 9th International Conference on

Conference_Location

Abu Dhabi

Type

conf

DOI

10.1109/Innovations.2013.6544411

Filename

6544411