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Relation Induction in Word Embeddings Revisited

Bouraoui, Zied, Jameel, Shoaib, Schockaert, Steven (2018) Relation Induction in Word Embeddings Revisited. In: The 27th International Conference on Computational Linguistics (COLING 2018). . pp. 1627-1637. ISBN 978-1-948087-50-6.

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Given a set of instances of some relation, the relation induction task is to predict which other word pairs are likely to be related in the same way. While it is natural to use word embeddings for this task, standard approaches based on vector translations turn out to perform poorly. To address this issue, we propose two probabilistic relation induction models. The first model is based on translations, but uses Gaussians to explicitly model the variability of these translations and to encode soft constraints on the source and target words that may be chosen. In the second model, we use Bayesian linear regression to encode the assumption that there is a linear relationship between the vector representations of related words, which is considerably weaker than the assumption underlying translation based models.

Item Type: Conference or workshop item (Proceeding)
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: Shoaib Jameel
Date Deposited: 11 Jun 2018 12:37 UTC
Last Modified: 29 May 2019 20:37 UTC
Resource URI: (The current URI for this page, for reference purposes)
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