Making Fast Graph-based Algorithms with Graph Metric Embeddings

Andrey Kutuzov, Mohammad Dorgham, Oleksiy Oliynyk, Chris Biemann, Alexander Panchenko


Abstract
Graph measures, such as node distances, are inefficient to compute. We explore dense vector representations as an effective way to approximate the same information. We introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks.
Anthology ID:
P19-1325
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3349–3355
Language:
URL:
https://aclanthology.org/P19-1325
DOI:
10.18653/v1/P19-1325
Bibkey:
Cite (ACL):
Andrey Kutuzov, Mohammad Dorgham, Oleksiy Oliynyk, Chris Biemann, and Alexander Panchenko. 2019. Making Fast Graph-based Algorithms with Graph Metric Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3349–3355, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Making Fast Graph-based Algorithms with Graph Metric Embeddings (Kutuzov et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1325.pdf
Code
 uhh-lt/path2vec
Data
DBpediaFB15k-237