Entity Linking for the Semantic Annotation of Italian Tweets

Linking entity mentions in Italian tweets to concepts in a knowledge base is a challenging task, due to the short and noisy nature of these short messages and the lack of specific resources for Italian. This paper proposes an adaptation of a general purpose Named Entity Linking algorithm, which expl...

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Autores principales:	Pierpaolo Basile, Giovanni Semeraro, Annalina Caputo
Formato:	article
Lenguaje:	EN
Publicado:	Accademia University Press 2016
Materias:	Social Sciences H Computational linguistics. Natural language processing P98-98.5
Acceso en línea:	https://doaj.org/article/8ef44d22af2a4b4eaf544fa37e911a23
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id	oai:doaj.org-article:8ef44d22af2a4b4eaf544fa37e911a23
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spelling	oai:doaj.org-article:8ef44d22af2a4b4eaf544fa37e911a232021-12-02T09:52:33ZEntity Linking for the Semantic Annotation of Italian Tweets2499-455310.4000/ijcol.362https://doaj.org/article/8ef44d22af2a4b4eaf544fa37e911a232016-06-01T00:00:00Zhttp://journals.openedition.org/ijcol/362https://doaj.org/toc/2499-4553Linking entity mentions in Italian tweets to concepts in a knowledge base is a challenging task, due to the short and noisy nature of these short messages and the lack of specific resources for Italian. This paper proposes an adaptation of a general purpose Named Entity Linking algorithm, which exploits the similarity measure computed over a Distributional Semantic Model, in the context of Italian tweets. In order to evaluate the proposed algorithm, we introduce a new dataset of tweets for entity linking that we have developed specifically for the Italian language.Pierpaolo BasileGiovanni SemeraroAnnalina CaputoAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 2, Iss 1, Pp 87-99 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Social Sciences H Computational linguistics. Natural language processing P98-98.5
spellingShingle	Social Sciences H Computational linguistics. Natural language processing P98-98.5 Pierpaolo Basile Giovanni Semeraro Annalina Caputo Entity Linking for the Semantic Annotation of Italian Tweets
description	Linking entity mentions in Italian tweets to concepts in a knowledge base is a challenging task, due to the short and noisy nature of these short messages and the lack of specific resources for Italian. This paper proposes an adaptation of a general purpose Named Entity Linking algorithm, which exploits the similarity measure computed over a Distributional Semantic Model, in the context of Italian tweets. In order to evaluate the proposed algorithm, we introduce a new dataset of tweets for entity linking that we have developed specifically for the Italian language.
format	article
author	Pierpaolo Basile Giovanni Semeraro Annalina Caputo
author_facet	Pierpaolo Basile Giovanni Semeraro Annalina Caputo
author_sort	Pierpaolo Basile
title	Entity Linking for the Semantic Annotation of Italian Tweets
title_short	Entity Linking for the Semantic Annotation of Italian Tweets
title_full	Entity Linking for the Semantic Annotation of Italian Tweets
title_fullStr	Entity Linking for the Semantic Annotation of Italian Tweets
title_full_unstemmed	Entity Linking for the Semantic Annotation of Italian Tweets
title_sort	entity linking for the semantic annotation of italian tweets
publisher	Accademia University Press
publishDate	2016
url	https://doaj.org/article/8ef44d22af2a4b4eaf544fa37e911a23
work_keys_str_mv	AT pierpaolobasile entitylinkingforthesemanticannotationofitaliantweets AT giovannisemeraro entitylinkingforthesemanticannotationofitaliantweets AT annalinacaputo entitylinkingforthesemanticannotationofitaliantweets
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Entity Linking for the Semantic Annotation of Italian Tweets

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