D-GloVe: A feasible least squares model for estimating word embedding densities

Jameel, Shoaib and Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 2016. D-GloVe: A feasible least squares model for estimating word embedding densities. Presented at: 26th International Conference on Computational Linguistics, Osaka, Japan, 11-16 December 2016.

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Abstract

We propose a new word embedding model, inspired by GloVe, which is formulated as a feasible least squares optimization problem. In contrast to existing models, we explicitly represent the uncertainty about the exact definition of each word vector. To this end, we estimate the error that results from using noisy co-occurrence counts in the formulation of the model, and we model the imprecision that results from including uninformative context words. Our experimental results demonstrate that this model compares favourably with existing word embedding models.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Funders:	ERC
Related URLs:	Organisation
Date of First Compliant Deposit:	12 October 2016
Last Modified:	01 Nov 2022 11:29
URI:	https://orca.cardiff.ac.uk/id/eprint/95131

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