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.- Anthology ID:
- D17-1132
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1283–1288
- Language:
- URL:
- https://aclanthology.org/D17-1132
- DOI:
- 10.18653/v1/D17-1132
- Cite (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.
- Cite (Informal):
- Predicting Word Association Strengths (Cattle & Ma, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D17-1132.pdf