Abstract
The existing word representation methods mostly limit their information source to word co-occurrence statistics. In this paper, we introduce ngrams into four representation methods: SGNS, GloVe, PPMI matrix, and its SVD factorization. Comprehensive experiments are conducted on word analogy and similarity tasks. The results show that improved word representations are learned from ngram co-occurrence statistics. We also demonstrate that the trained ngram representations are useful in many aspects such as finding antonyms and collocations. Besides, a novel approach of building co-occurrence matrix is proposed to alleviate the hardware burdens brought by ngrams.- Anthology ID:
- D17-1023
- Erratum e1:
- D17-1023e1
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 244–253
- Language:
- URL:
- https://aclanthology.org/D17-1023
- DOI:
- 10.18653/v1/D17-1023
- Cite (ACL):
- Zhe Zhao, Tao Liu, Shen Li, Bofang Li, and Xiaoyong Du. 2017. Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 244–253, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Ngram2vec: Learning Improved Word Representations from Ngram Co-occurrence Statistics (Zhao et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D17-1023.pdf