Recurrent Context Window Networks for Italian Named Entity Recognizer

In this paper, we introduce a Deep Neural Network (DNN) for engineering Named Entity Recognizers (NERs) in Italian. Our network uses a sliding window of word contexts to predict tags. It relies on a simple word-level log-likelihood as a cost function and uses a new recurrent feedback mechanism to en...

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Autores principales:	Daniele Bonadiman, Alessandro Moschitti, Aliaksei Severyn
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/4524f7e164484008a35bc7a5fbe1b5b2
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spelling	oai:doaj.org-article:4524f7e164484008a35bc7a5fbe1b5b22021-12-02T09:52:33ZRecurrent Context Window Networks for Italian Named Entity Recognizer2499-455310.4000/ijcol.358https://doaj.org/article/4524f7e164484008a35bc7a5fbe1b5b22016-06-01T00:00:00Zhttp://journals.openedition.org/ijcol/358https://doaj.org/toc/2499-4553In this paper, we introduce a Deep Neural Network (DNN) for engineering Named Entity Recognizers (NERs) in Italian. Our network uses a sliding window of word contexts to predict tags. It relies on a simple word-level log-likelihood as a cost function and uses a new recurrent feedback mechanism to ensure that the dependencies between the output tags are properly modeled. These choices make our network simple and computationally efficient. Unlike previous best NERs for Italian, our model does not require manual-designed features, external parsers or additional resources. The evaluation on the Evalita 2009 benchmark shows that our DNN performs on par with the best NERs, outperforming the state of the art when gazetteer features are used.Daniele BonadimanAlessandro MoschittiAliaksei SeverynAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 2, Iss 1, Pp 77-86 (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 Daniele Bonadiman Alessandro Moschitti Aliaksei Severyn Recurrent Context Window Networks for Italian Named Entity Recognizer
description	In this paper, we introduce a Deep Neural Network (DNN) for engineering Named Entity Recognizers (NERs) in Italian. Our network uses a sliding window of word contexts to predict tags. It relies on a simple word-level log-likelihood as a cost function and uses a new recurrent feedback mechanism to ensure that the dependencies between the output tags are properly modeled. These choices make our network simple and computationally efficient. Unlike previous best NERs for Italian, our model does not require manual-designed features, external parsers or additional resources. The evaluation on the Evalita 2009 benchmark shows that our DNN performs on par with the best NERs, outperforming the state of the art when gazetteer features are used.
format	article
author	Daniele Bonadiman Alessandro Moschitti Aliaksei Severyn
author_facet	Daniele Bonadiman Alessandro Moschitti Aliaksei Severyn
author_sort	Daniele Bonadiman
title	Recurrent Context Window Networks for Italian Named Entity Recognizer
title_short	Recurrent Context Window Networks for Italian Named Entity Recognizer
title_full	Recurrent Context Window Networks for Italian Named Entity Recognizer
title_fullStr	Recurrent Context Window Networks for Italian Named Entity Recognizer
title_full_unstemmed	Recurrent Context Window Networks for Italian Named Entity Recognizer
title_sort	recurrent context window networks for italian named entity recognizer
publisher	Accademia University Press
publishDate	2016
url	https://doaj.org/article/4524f7e164484008a35bc7a5fbe1b5b2
work_keys_str_mv	AT danielebonadiman recurrentcontextwindownetworksforitaliannamedentityrecognizer AT alessandromoschitti recurrentcontextwindownetworksforitaliannamedentityrecognizer AT aliakseiseveryn recurrentcontextwindownetworksforitaliannamedentityrecognizer
_version_	1718397940636057600

Recurrent Context Window Networks for Italian Named Entity Recognizer

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