DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding
Abstract
Phonetic similarity algorithms identify words and phrases with similar pronunciation which are used in many natural language processing tasks. However, existing approaches are designed mainly for Indo-European languages and fail to capture the unique properties of Chinese pronunciation. In this paper, we propose a high dimensional encoded phonetic similarity algorithm for Chinese, DIMSIM. The encodings are learned from annotated data to separately map initial and final phonemes into n-dimensional coordinates. Pinyin phonetic similarities are then calculated by aggregating the similarities of initial, final and tone. DIMSIM demonstrates a 7.5X improvement on mean reciprocal rank over the state-of-the-art phonetic similarity approaches.- Anthology ID:
- K18-1043
- Volume:
- Proceedings of the 22nd Conference on Computational Natural Language Learning
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Anna Korhonen, Ivan Titov
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 444–453
- Language:
- URL:
- https://aclanthology.org/K18-1043
- DOI:
- 10.18653/v1/K18-1043
- Cite (ACL):
- Min Li, Marina Danilevsky, Sara Noeman, and Yunyao Li. 2018. DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 444–453, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- DIMSIM: An Accurate Chinese Phonetic Similarity Algorithm Based on Learned High Dimensional Encoding (Li et al., CoNLL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/K18-1043.pdf
- Code
- System-T/DimSim