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Kanopy4Tweets: Entity Extraction and Linking for Twitter

Authors: 

Pablo Torres Tramon, Hugo Hromic, Brian Walsh, Bahareh Rahmanzadeh Heravi, Conor Hayes

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Named Entity rEcognition and Linking (NEEL) from text is an essential task in many Natural Language Processing (NLP) applications because it enables a better understanding of the content. However in the context of Social Media, NEEL is challenging due to the higher level of writing mistakes, fast language dynamics and often lack of context. To this end, we adapted Kanopy – an unsupervised graph-based topic disambiguation system – to be used for the task of NEEL in the domain of Twitter, a fast-paced micro-blogging platform. We describe the design of our solution and report the results obtained by our system using the official corpus of Tweets for the NEEL 2016 Challenge [10].
Conference Name: 
WWW2016
Proceedings: 
6th Workshop on Making Sense of Microposts
Digital Object Identifer (DOI): 
10.XXX
Publication Date: 
11/04/2016
Conference Location: 
Canada
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
Yes
Publication document: