@inproceedings{kutuzov-etal-2019-learning,
title = "Learning Graph Embeddings from {W}ord{N}et-based Similarity Measures",
author = "Kutuzov, Andrey and
Dorgham, Mohammad and
Oliynyk, Oleksiy and
Biemann, Chris and
Panchenko, Alexander",
editor = "Mihalcea, Rada and
Shutova, Ekaterina and
Ku, Lun-Wei and
Evang, Kilian and
Poria, Soujanya",
booktitle = "Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-1014",
doi = "10.18653/v1/S19-1014",
pages = "125--135",
abstract = "We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities. The model learns representations for nodes in a dense space that approximate a given user-defined graph distance measure, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNet-based similarity measures, show that our approach yields competitive results, outperforming strong graph embedding baselines. The model is computationally efficient, being orders of magnitude faster than the direct computation of graph-based distances.",
}
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<abstract>We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities. The model learns representations for nodes in a dense space that approximate a given user-defined graph distance measure, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNet-based similarity measures, show that our approach yields competitive results, outperforming strong graph embedding baselines. The model is computationally efficient, being orders of magnitude faster than the direct computation of graph-based distances.</abstract>
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%0 Conference Proceedings
%T Learning Graph Embeddings from WordNet-based Similarity Measures
%A Kutuzov, Andrey
%A Dorgham, Mohammad
%A Oliynyk, Oleksiy
%A Biemann, Chris
%A Panchenko, Alexander
%Y Mihalcea, Rada
%Y Shutova, Ekaterina
%Y Ku, Lun-Wei
%Y Evang, Kilian
%Y Poria, Soujanya
%S Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F kutuzov-etal-2019-learning
%X We present path2vec, a new approach for learning graph embeddings that relies on structural measures of pairwise node similarities. The model learns representations for nodes in a dense space that approximate a given user-defined graph distance measure, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. Evaluation of the proposed model on semantic similarity and word sense disambiguation tasks, using various WordNet-based similarity measures, show that our approach yields competitive results, outperforming strong graph embedding baselines. The model is computationally efficient, being orders of magnitude faster than the direct computation of graph-based distances.
%R 10.18653/v1/S19-1014
%U https://aclanthology.org/S19-1014
%U https://doi.org/10.18653/v1/S19-1014
%P 125-135
Markdown (Informal)
[Learning Graph Embeddings from WordNet-based Similarity Measures](https://aclanthology.org/S19-1014) (Kutuzov et al., *SEM 2019)
ACL
- Andrey Kutuzov, Mohammad Dorgham, Oleksiy Oliynyk, Chris Biemann, and Alexander Panchenko. 2019. Learning Graph Embeddings from WordNet-based Similarity Measures. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 125–135, Minneapolis, Minnesota. Association for Computational Linguistics.