@inproceedings{santus-etal-2018-rank,
title = "A Rank-Based Similarity Metric for Word Embeddings",
author = "Santus, Enrico and
Wang, Hongmin and
Chersoni, Emmanuele and
Zhang, Yue",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2088",
doi = "10.18653/v1/P18-2088",
pages = "552--557",
abstract = "Word Embeddings have recently imposed themselves as a standard for representing word meaning in NLP. Semantic similarity between word pairs has become the most common evaluation benchmark for these representations, with vector cosine being typically used as the only similarity metric. In this paper, we report experiments with a rank-based metric for WE, which performs comparably to vector cosine in similarity estimation and outperforms it in the recently-introduced and challenging task of outlier detection, thus suggesting that rank-based measures can improve clustering quality.",
}
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%0 Conference Proceedings
%T A Rank-Based Similarity Metric for Word Embeddings
%A Santus, Enrico
%A Wang, Hongmin
%A Chersoni, Emmanuele
%A Zhang, Yue
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F santus-etal-2018-rank
%X Word Embeddings have recently imposed themselves as a standard for representing word meaning in NLP. Semantic similarity between word pairs has become the most common evaluation benchmark for these representations, with vector cosine being typically used as the only similarity metric. In this paper, we report experiments with a rank-based metric for WE, which performs comparably to vector cosine in similarity estimation and outperforms it in the recently-introduced and challenging task of outlier detection, thus suggesting that rank-based measures can improve clustering quality.
%R 10.18653/v1/P18-2088
%U https://aclanthology.org/P18-2088
%U https://doi.org/10.18653/v1/P18-2088
%P 552-557
Markdown (Informal)
[A Rank-Based Similarity Metric for Word Embeddings](https://aclanthology.org/P18-2088) (Santus et al., ACL 2018)
ACL
- Enrico Santus, Hongmin Wang, Emmanuele Chersoni, and Yue Zhang. 2018. A Rank-Based Similarity Metric for Word Embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 552–557, Melbourne, Australia. Association for Computational Linguistics.