Abstract
Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations. In this paper, a metric is designed to estimate second order co-occurrence relations based on context overlap. The estimated values are further used as the augmented data to enhance the learning of word embeddings by joint training with existing neural word embedding models. Experimental results show that better word vectors can be obtained for word similarity tasks and some downstream NLP tasks by the enhanced approach.- Anthology ID:
- D18-1057
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 587–593
- Language:
- URL:
- https://aclanthology.org/D18-1057
- DOI:
- 10.18653/v1/D18-1057
- Cite (ACL):
- Yimeng Zhuang, Jinghui Xie, Yinhe Zheng, and Xuan Zhu. 2018. Quantifying Context Overlap for Training Word Embeddings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 587–593, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Quantifying Context Overlap for Training Word Embeddings (Zhuang et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D18-1057.pdf
- Data
- SST