Abstract
Word embeddings learned using the distributional hypothesis (e.g., GloVe, Word2vec) are good at encoding various lexical-semantic relations. However, they do not capture the emotion aspects of words. We present a novel retrofitting method for updating the vectors of emotion bearing words like fun, offence, angry, etc. The retrofitted embeddings achieve better inter-cluster and intra-cluster distance for words having the same emotions, e.g., the joy cluster containing words like fun, happiness, etc., and the anger cluster with words like offence, rage, etc., as evaluated through different cluster quality metrics. For the downstream tasks on sentiment analysis and sarcasm detection, simple classification models, such as SVM and Attention Net, learned using our retrofitted embeddings perform better than their pre-trained counterparts (about 1.5 % improvement in F1-score) as well as other benchmarks. Furthermore, the difference in performance is more pronounced in the limited data setting.- Anthology ID:
- 2022.coling-1.363
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 4136–4148
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.363
- DOI:
- Cite (ACL):
- Sapan Shah, Sreedhar Reddy, and Pushpak Bhattacharyya. 2022. Emotion Enriched Retrofitted Word Embeddings. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4136–4148, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Emotion Enriched Retrofitted Word Embeddings (Shah et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.coling-1.363.pdf
- Data
- MUStARD++