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Entity Linking For Tweets Using User Model and Real-time News Stream  

Jeong, Soyoon (Computer Science and Engineering Sogang University)
Park, Youngmin (Computer Science and Engineering Sogang University)
Kang, Sangwoo (Computer Science and Engineering Sogang University)
Seo, Jungyun (Computer Science and Engineering Sogang University)
Publication Information
Korean Journal of Cognitive Science / v.26, no.4, 2015 , pp. 435-452 More about this Journal
Abstract
Recent researches on Entity Linking(EL) have attempted to disambiguate entities by using a knowledge base to handle the semantic relatedness and up-to-date information. However, EL for tweets using a knowledge base is still unsatisfactory, mainly because the tweet data are mostly composed of short and noisy contexts and real-time issues. The EL system the present work builds up links ambiguous entities to the corresponding entries in a given knowledge base via exploring the news articles and the user history. Using news articles, the system can overcome the problem of Wikipedia coverage (i.e., not handling real-time issues). In addition, given that users usually post tweets related to their particular interests, the current system referring to the user history robustly and effectively works with a small size of tweet data. In this paper, we propose an approach to building an EL system that links ambiguous entities to the corresponding entries in a given knowledge base through the news articles and the user history. We created a dataset of Korean tweets including ambiguous entities randomly selected from the extracted tweets over a seven-day period and evaluated the system using this dataset. We use accuracy index(number of correct answer given by system/number of data set) The experimental results show that our system achieves a accuracy of 67.7% and outperforms the EL methods that exclusively use a knowledge base.
Keywords
Named Entity Linking; Entity Linking; Entity Disambiguation; Twotter; Wikipedia;
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