Exploiting Morphological Regularities in Distributional Word Representations
Arihant Gupta, Syed Sarfaraz Akhtar, Avijit Vajpayee, Arjit Srivastava, Madan Gopal Jhanwar, Manish Shrivastava
Abstract
We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces. We propose a novel method for generating embeddings of words from their morphological variants using morphological transformation operators. We evaluate this approach on MSR word analogy test set with an accuracy of 85% which is 12% higher than the previous best known system.- Anthology ID:
- D17-1028
- Original:
- D17-1028v1
- Version 2:
- D17-1028v2
- 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:
- 292–297
- Language:
- URL:
- https://aclanthology.org/D17-1028
- DOI:
- 10.18653/v1/D17-1028
- Cite (ACL):
- Arihant Gupta, Syed Sarfaraz Akhtar, Avijit Vajpayee, Arjit Srivastava, Madan Gopal Jhanwar, and Manish Shrivastava. 2017. Exploiting Morphological Regularities in Distributional Word Representations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 292–297, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Exploiting Morphological Regularities in Distributional Word Representations (Gupta et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D17-1028.pdf