Sales, Juliano Efson and Souza, Leonardo and Barzegar, Siamak and Davis, Brian and Freitas, Andre and Handschuh, Siegfried (2018) Indra: A word embedding and semantic relatedness server. In: LREC 2018, Eleventh International Conference on Language Resources and Evaluation. European Language Resources Association, pp. 1326-1332. ISBN 9791095546009

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Abstract

In recent years word embedding/distributional semantic models evolved to become a fundamental component in many natural language processing (NLP) architectures due to their ability of capturing and quantifying semantic associations at scale. Word embedding models can be used to satisfy recurrent tasks in NLP such as lexical and semantic generalisation in machine learning tasks, finding similar or related words and computing semantic relatedness of terms. However, building and consuming specific word embedding models require the setting of a large set of configurations, such as corpus-dependant parameters, distance measures as well as compositional models. Despite their increasing relevance as a component in NLP architectures, existing frameworks provide limited options in their ability to systematically build, parametrise, compare and evaluate different models. To answer this demand, this paper describes INDRA, a multi-lingual word embedding/distributional semantics framework which supports the creation, use and evaluation of word embedding models. In addition to the tool, INDRA also shares more than 65 pre-computed models in 14 languages.

Item Type:	Book Section
Additional Information:	The LREC 2018 Proceedings are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License https://creativecommons.org/licenses/by-nc/4.0/
Keywords:	word embedding server; semantic relatedness server; semantic toolkit; corpus pre-processor;
Academic Unit:	Faculty of Science and Engineering > Computer Science
Item ID:	13407
Depositing User:	Brian Davis
Date Deposited:	08 Oct 2020 13:53
Publisher:	European Language Resources Association
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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