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            <namePart type="given">Emily</namePart>
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    <abstract>We present SeVeN (Semantic Vector Networks), a hybrid resource that encodes relationships between words in the form of a graph. Different from traditional semantic networks, these relations are represented as vectors in a continuous vector space. We propose a simple pipeline for learning such relation vectors, which is based on word vector averaging in combination with an ad hoc autoencoder. We show that by explicitly encoding relational information in a dedicated vector space we can capture aspects of word meaning that are complementary to what is captured by word embeddings. For example, by examining clusters of relation vectors, we observe that relational similarities can be identified at a more abstract level than with traditional word vector differences. Finally, we test the effectiveness of semantic vector networks in two tasks: measuring word similarity and neural text categorization. SeVeN is available at bitbucket.org/luisespinosa/seven.</abstract>
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%0 Conference Proceedings
%T SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors
%A Espinosa-Anke, Luis
%A Schockaert, Steven
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F espinosa-anke-schockaert-2018-seven
%X We present SeVeN (Semantic Vector Networks), a hybrid resource that encodes relationships between words in the form of a graph. Different from traditional semantic networks, these relations are represented as vectors in a continuous vector space. We propose a simple pipeline for learning such relation vectors, which is based on word vector averaging in combination with an ad hoc autoencoder. We show that by explicitly encoding relational information in a dedicated vector space we can capture aspects of word meaning that are complementary to what is captured by word embeddings. For example, by examining clusters of relation vectors, we observe that relational similarities can be identified at a more abstract level than with traditional word vector differences. Finally, we test the effectiveness of semantic vector networks in two tasks: measuring word similarity and neural text categorization. SeVeN is available at bitbucket.org/luisespinosa/seven.
%U https://aclanthology.org/C18-1225
%P 2653-2665

Markdown (Informal)

[SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors](https://aclanthology.org/C18-1225) (Espinosa-Anke & Schockaert, COLING 2018)

• SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors (Espinosa-Anke & Schockaert, COLING 2018)

ACL

• Luis Espinosa-Anke and Steven Schockaert. 2018. SeVeN: Augmenting Word Embeddings with Unsupervised Relation Vectors. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2653–2665, Santa Fe, New Mexico, USA. Association for Computational Linguistics.