Ivan Sanchez Carmona, V;
Riedel, S;
(2017)
How Well Can We Predict Hypernyms from Word Embeddings?
A Dataset-Centric Analysis.
In:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers.
(pp. pp. 401-407).
Association for Computational Linguistics: Valencia, Spain.
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Abstract
One key property of word embeddings currently under study is their capacity to encode hypernymy. Previous works have used supervised models to recover hypernymy structures from embeddings. However, the overall results do not clearly show how well we can recover such structures. We conduct the first dataset-centric analysis that shows how only the Baroni dataset provides consistent results. We empirically show that a possible reason for its good performance is its alignment to dimensions specific of hypernymy: generality and similarity.
| Type: | Proceedings paper |
|---|---|
| Title: | How Well Can We Predict Hypernyms from Word Embeddings? A Dataset-Centric Analysis |
| Event: | 15th Conference of the European Chapter of the Association for Computational Linguistics |
| ISBN-13: | 9781510838604 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://aclweb.org/anthology/E17-2064 |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10064232 |
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