The Journal of Machine Learning Research
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Detection of Bloggers' Interests: Using Textual, Temporal, and Interactive Features
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Learning to link with wikipedia
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Collective annotation of Wikipedia entities in web text
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Using domain knowledge for ontology-guided entity extraction from noisy, unstructured text data
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Microblogging for Language Learning: Using Twitter to Train Communicative and Cultural Competence
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Tweet the debates: understanding community annotation of uncollected sources
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Twitter power: Tweets as electronic word of mouth
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TwitterRank: finding topic-sensitive influential twitterers
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Inferring who-is-who in the Twitter social network
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WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
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RESLVE: leveraging user interest to improve entity disambiguation on short text
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Interest classification of Twitter users using Wikipedia
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Two Phase Extraction Method for Multi-label Classification of Real Life Tweets
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Twitter, a micro-blogging service, provides users with a framework for writing brief, often-noisy postings about their lives. These posts are called "Tweets." In this paper we present early results on discovering Twitter users' topics of interest by examining the entities they mention in their Tweets. Our approach leverages a knowledge base to disambiguate and categorize the entities in the Tweets. We then develop a "topic profile," which characterizes users' topics of interest, by discerning which categories appear frequently and cover the entities. We demonstrate that even in this early work we are able to successfully discover the main topics of interest for the users in our study.