Skip to main content

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. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. . pp. 4911-4917. IJCAI ISBN 978-0-9992411-4-1. (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) (KAR id:74586)

PDF Author's Accepted Manuscript
Language: English

Restricted to Repository staff only
Contact us about this Publication
[thumbnail of jose_ijcai_2019.pdf]
Official URL:
https://doi.org/10.24963/ijcai.2019/682

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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Shoaib Jameel
Date Deposited: 26 Jun 2019 09:16 UTC
Last Modified: 16 Feb 2021 14:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/74586 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.