@inproceedings{cattle-ma-2017-predicting,
title = "Predicting Word Association Strengths",
author = "Cattle, Andrew and
Ma, Xiaojuan",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1132",
doi = "10.18653/v1/D17-1132",
pages = "1283--1288",
abstract = "This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973). We achieve results of r=0.357 and p=0.379, r=0.344 and p=0.300, and r=0.292 and p=0.363, respectively. We find Word2Vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014) cosine similarities, as well as vector offsets, to be the highest performing features. Furthermore, we examine the usefulness of Gaussian embeddings (Vilnis and McCallum, 2014) for predicting word association strength, the first work to do so.",
}
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%0 Conference Proceedings
%T Predicting Word Association Strengths
%A Cattle, Andrew
%A Ma, Xiaojuan
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F cattle-ma-2017-predicting
%X This paper looks at the task of predicting word association strengths across three datasets; WordNet Evocation (Boyd-Graber et al., 2006), University of Southern Florida Free Association norms (Nelson et al., 2004), and Edinburgh Associative Thesaurus (Kiss et al., 1973). We achieve results of r=0.357 and p=0.379, r=0.344 and p=0.300, and r=0.292 and p=0.363, respectively. We find Word2Vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014) cosine similarities, as well as vector offsets, to be the highest performing features. Furthermore, we examine the usefulness of Gaussian embeddings (Vilnis and McCallum, 2014) for predicting word association strength, the first work to do so.
%R 10.18653/v1/D17-1132
%U https://aclanthology.org/D17-1132
%U https://doi.org/10.18653/v1/D17-1132
%P 1283-1288
Markdown (Informal)
[Predicting Word Association Strengths](https://aclanthology.org/D17-1132) (Cattle & Ma, EMNLP 2017)
ACL
- Andrew Cattle and Xiaojuan Ma. 2017. Predicting Word Association Strengths. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1283–1288, Copenhagen, Denmark. Association for Computational Linguistics.