Kanopy4Tweets: Entity extraction and linking for twitter

Pablo Torres-Tramón; Hugo Hromic; Brian Walsh; Bahareh R. Heravi; Conor Hayes

Kanopy4Tweets: Entity extraction and linking for twitter

Pablo Torres-Tramón, Hugo Hromic, Brian Walsh, Bahareh R. Heravi, Conor Hayes

Insight Centre for Data Analytics Galway

Research output: Contribution to a Journal (Peer & Non Peer) › Conference article › peer-review

7 Citations (Scopus)

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.

Original language	English
Pages (from-to)	64-66
Number of pages	3
Journal	CEUR Workshop Proceedings
Volume	1691
Publication status	Published - 2016
Externally published	Yes
Event	6th Workshop on 'Making Sense of Microposts', Microposts 2016 - Montreal, Canada Duration: 11 Apr 2016 → …

Keywords

Entity linking
Information extraction
Twitter

Cite this

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title = "Kanopy4Tweets: Entity extraction and linking for twitter",

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.",

keywords = "Entity linking, Information extraction, Twitter",

author = "Pablo Torres-Tram{\'o}n and Hugo Hromic and Brian Walsh and Heravi, \{Bahareh R.\} and Conor Hayes",

note = "Publisher Copyright: Copyright {\textcopyright} 2016 held by author(s)/owner(s).; 6th Workshop on 'Making Sense of Microposts', Microposts 2016 ; Conference date: 11-04-2016",

year = "2016",

language = "English",

volume = "1691",

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journal = "CEUR Workshop Proceedings",

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TY - JOUR

T1 - Kanopy4Tweets

T2 - 6th Workshop on 'Making Sense of Microposts', Microposts 2016

AU - Torres-Tramón, Pablo

AU - Hromic, Hugo

AU - Walsh, Brian

AU - Heravi, Bahareh R.

AU - Hayes, Conor

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Entity linking

KW - Information extraction

KW - Twitter

UR - https://www.scopus.com/pages/publications/84992425343

M3 - Conference article

AN - SCOPUS:84992425343

SN - 1613-0073

VL - 1691

SP - 64

EP - 66

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 11 April 2016

ER -

Kanopy4Tweets: Entity extraction and linking for twitter

Abstract

Keywords

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