@inproceedings{macavaney-zeldes-2018-deeper,
title = "A Deeper Look into Dependency-Based Word Embeddings",
author = "MacAvaney, Sean and
Zeldes, Amir",
editor = "Cordeiro, Silvio Ricardo and
Oraby, Shereen and
Pavalanathan, Umashanthi and
Rim, Kyeongmin",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-4006",
doi = "10.18653/v1/N18-4006",
pages = "40--45",
abstract = "We investigate the effect of various dependency-based word embeddings on distinguishing between functional and domain similarity, word similarity rankings, and two downstream tasks in English. Variations include word embeddings trained using context windows from Stanford and Universal dependencies at several levels of enhancement (ranging from unlabeled, to Enhanced++ dependencies). Results are compared to basic linear contexts and evaluated on several datasets. We found that embeddings trained with Universal and Stanford dependency contexts excel at different tasks, and that enhanced dependencies often improve performance.",
}
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%0 Conference Proceedings
%T A Deeper Look into Dependency-Based Word Embeddings
%A MacAvaney, Sean
%A Zeldes, Amir
%Y Cordeiro, Silvio Ricardo
%Y Oraby, Shereen
%Y Pavalanathan, Umashanthi
%Y Rim, Kyeongmin
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F macavaney-zeldes-2018-deeper
%X We investigate the effect of various dependency-based word embeddings on distinguishing between functional and domain similarity, word similarity rankings, and two downstream tasks in English. Variations include word embeddings trained using context windows from Stanford and Universal dependencies at several levels of enhancement (ranging from unlabeled, to Enhanced++ dependencies). Results are compared to basic linear contexts and evaluated on several datasets. We found that embeddings trained with Universal and Stanford dependency contexts excel at different tasks, and that enhanced dependencies often improve performance.
%R 10.18653/v1/N18-4006
%U https://aclanthology.org/N18-4006
%U https://doi.org/10.18653/v1/N18-4006
%P 40-45
Markdown (Informal)
[A Deeper Look into Dependency-Based Word Embeddings](https://aclanthology.org/N18-4006) (MacAvaney & Zeldes, NAACL 2018)
ACL
- Sean MacAvaney and Amir Zeldes. 2018. A Deeper Look into Dependency-Based Word Embeddings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 40–45, New Orleans, Louisiana, USA. Association for Computational Linguistics.