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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. . (In press) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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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)
Divisions: Faculties > Sciences > School of Computing
Depositing User: Shoaib Jameel
Date Deposited: 26 Jun 2019 09:16 UTC
Last Modified: 05 Aug 2019 10:04 UTC
Resource URI: (The current URI for this page, for reference purposes)
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