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A Latent Variable Model for Learning Distributional Relation Vectors

Camacho-Collados, Jose, Espinosa-Anke, Luis, Jameel, Shoaib, Schockaert, Steven (2019) A Latent Variable Model for Learning Distributional Relation Vectors. In: International Joint Conferences on Artificial Intelligence. A Latent Variable Model for Learning Distributional Relation Vectors Jose Camacho-Collados, Luis Espinosa-Anke, Shoaib Jameel, Steven Schockaert Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. . pp. 4911-4917. International Joint Conferences on Artificial Intelligence Organization ISBN 978-0-9992411-4-1. (In press) (doi:10.24963/ijcai.2019/682) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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Abstract

Recently a number of unsupervised approaches have been proposed for learning vectors that capture the relationship between two words. Inspired by word embedding models, these approaches rely on co-occurrence statistics that are obtained from sentences in which the two target words appear. However, the number of such sentences is often quite small, and most of the words that occur in them are not relevant for characterizing the considered relationship. As a result, standard cooccurrence statistics typically lead to noisy relation vectors. To address this issue, we propose

a latent variable model that aims to explicitly determine what words from the given sentences best characterize the relationship between the two target words. Relation vectors then correspond to the parameters of a simple unigram language model which is estimated from these words.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.24963/ijcai.2019/682
Divisions: Faculties > Sciences > School of Computing
Depositing User: Shoaib Jameel
Date Deposited: 26 Jun 2019 09:16 UTC
Last Modified: 03 Oct 2019 11:11 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/74586 (The current URI for this page, for reference purposes)
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