Eisner, B;
Rocktäschel, T;
Augenstein, I;
Bošnjak, M;
Riedel, S;
(2016)
emoji2vec: Learning Emoji Representations from their Description.
In:
Proceedings of The Fourth International Workshop on Natural Language Processing for Social Media.
(pp. pp. 48-54).
Association for Computational Linguistics: Austin, TX, USA.
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Abstract
Many current natural language processing applications for social media rely on representation learning and utilize pre-trained word embeddings. There currently exist several publicly-available, pre-trained sets of word embeddings, but they contain few or no emoji representations even as emoji usage in social media has increased. In this paper we release emoji2vec, pre-trained embeddings for all Unicode emoji which are learned from their description in the Unicode emoji standard. The resulting emoji embeddings can be readily used in downstream social natural language processing applications alongside word2vec. We demonstrate, for the downstream task of sentiment analysis, that emoji embeddings learned from short descriptions outperforms a skip-gram model trained on a large collection of tweets, while avoiding the need for contexts in which emoji need to appear frequently in order to estimate a representation.
| Type: | Proceedings paper |
|---|---|
| Title: | emoji2vec: Learning Emoji Representations from their Description |
| Event: | The Fourth International Workshop on Natural Language Processing for Social Media |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.18653/v1/W16-6208 |
| Publisher version: | https://doi.org/10.18653/v1/W16-6208 |
| Language: | English |
| Additional information: | ACL materials are Copyright © 1963-2018 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 License. |
| Keywords: | cs.CL, cs.CL, 68T50, I.2.7 |
| 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-pp.ucl.ac.uk/id/eprint/1527667 |
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