Event Knowledge in Compositional Distributional Semantics

The great majority of compositional models in distributional semantics present methods to compose vectors or tensors in a representation of the sentence. Here we propose to enrich one of the best performing methods (vector addition, which we take as a baseline) with distributional knowledge about ev...

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Autores principales:	Ludovica Pannitto, Alessandro Lenci
Formato:	article
Lenguaje:	EN
Publicado:	Accademia University Press 2019
Materias:	Social Sciences H Computational linguistics. Natural language processing P98-98.5
Acceso en línea:	https://doaj.org/article/49c7977264824af08efff4114877b834
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id	oai:doaj.org-article:49c7977264824af08efff4114877b834
record_format	dspace
spelling	oai:doaj.org-article:49c7977264824af08efff4114877b8342021-12-02T09:52:23ZEvent Knowledge in Compositional Distributional Semantics2499-455310.4000/ijcol.463https://doaj.org/article/49c7977264824af08efff4114877b8342019-06-01T00:00:00Zhttp://journals.openedition.org/ijcol/463https://doaj.org/toc/2499-4553The great majority of compositional models in distributional semantics present methods to compose vectors or tensors in a representation of the sentence. Here we propose to enrich one of the best performing methods (vector addition, which we take as a baseline) with distributional knowledge about events. The resulting model is able to outperform our baseline.Ludovica PannittoAlessandro LenciAccademia University PressarticleSocial SciencesHComputational linguistics. Natural language processingP98-98.5ENIJCoL, Vol 5, Iss 1, Pp 73-88 (2019)
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 Ludovica Pannitto Alessandro Lenci Event Knowledge in Compositional Distributional Semantics
description	The great majority of compositional models in distributional semantics present methods to compose vectors or tensors in a representation of the sentence. Here we propose to enrich one of the best performing methods (vector addition, which we take as a baseline) with distributional knowledge about events. The resulting model is able to outperform our baseline.
format	article
author	Ludovica Pannitto Alessandro Lenci
author_facet	Ludovica Pannitto Alessandro Lenci
author_sort	Ludovica Pannitto
title	Event Knowledge in Compositional Distributional Semantics
title_short	Event Knowledge in Compositional Distributional Semantics
title_full	Event Knowledge in Compositional Distributional Semantics
title_fullStr	Event Knowledge in Compositional Distributional Semantics
title_full_unstemmed	Event Knowledge in Compositional Distributional Semantics
title_sort	event knowledge in compositional distributional semantics
publisher	Accademia University Press
publishDate	2019
url	https://doaj.org/article/49c7977264824af08efff4114877b834
work_keys_str_mv	AT ludovicapannitto eventknowledgeincompositionaldistributionalsemantics AT alessandrolenci eventknowledgeincompositionaldistributionalsemantics
_version_	1718397942180610048

Event Knowledge in Compositional Distributional Semantics

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