2013 LinkingNamedEntitiesinTweetswit

• (Shen et al., 2013) ⇒ Wei Shen, Jianyong Wang, Ping Luo, and Min Wang. (2013). “Linking Named Entities in Tweets with Knowledge Base via User Interest Modeling.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487686

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Linking+Named+Entities+in+Tweets+with+Knowledge+Base+via+User+Interest+Modeling
• http://dl.acm.org/citation.cfm?id=2487575.2487686&preflayout=flat#citedby

Quotes

Author Keywords

• Information search and retrieval; knowledge base; tweet entity linking; user interest modeling

Abstract

Twitter has become an increasingly important source of information, with more than 400 million tweets posted per day. The task to link the named entity mentions detected from tweets with the corresponding real world entities in the knowledge base is called tweet entity linking. This task is of practical importance and can facilitate many different tasks, such as personalized recommendation and user interest discovery. The tweet entity linking task is challenging due to the noisy, short, and informal nature of tweets. Previous methods focus on linking entities in Web documents, and largely rely on the context around the entity mention and the topical coherence between entities in the document. However, these methods cannot be effectively applied to the tweet entity linking task due to the insufficient context information contained in a tweet. In this paper, we propose KAURI, a graph-based framework to collectively link all the named entity mentions in all tweets posted by a user via modeling the user's topics of interest. Our assumption is that each user has an underlying topic interest distribution over various named entities. KAURI integrates the intra-tweet local information with the inter-tweet user interest information into a unified graph-based framework. We extensively evaluated the performance of KAURI over manually annotated tweet corpus, and the experimental results show that KAURI significantly outperforms the baseline methods in terms of accuracy, and KAURI is efficient and scales well to tweet stream.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 LinkingNamedEntitiesinTweetswit	Jianyong Wang Ping Luo Min Wang Wei Shen			Linking Named Entities in Tweets with Knowledge Base via User Interest Modeling				10.1145/2487575.2487686		2013