Abstract
To improve word embedding, subword information has been widely employed in state-of-the-art methods. These methods can be classified to either compositional or predictive models. In this paper, we propose a hybrid learning scheme, which integrates compositional and predictive model for word embedding. Such a scheme can take advantage of both models, thus effectively learning word embedding. The proposed scheme has been applied to learn word representation on Chinese. Our results show that the proposed scheme can significantly improve the performance of word embedding in terms of analogical reasoning and is robust to the size of training data.- Anthology ID:
- W18-3011
- Volume:
- Proceedings of the Third Workshop on Representation Learning for NLP
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Isabelle Augenstein, Kris Cao, He He, Felix Hill, Spandana Gella, Jamie Kiros, Hongyuan Mei, Dipendra Misra
- Venue:
- RepL4NLP
- SIG:
- SIGREP
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 84–90
- Language:
- URL:
- https://aclanthology.org/W18-3011
- DOI:
- 10.18653/v1/W18-3011
- Cite (ACL):
- Wenfan Chen and Weiguo Sheng. 2018. A Hybrid Learning Scheme for Chinese Word Embedding. In Proceedings of the Third Workshop on Representation Learning for NLP, pages 84–90, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- A Hybrid Learning Scheme for Chinese Word Embedding (Chen & Sheng, RepL4NLP 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-3011.pdf