PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors
David R. Mortensen, Patrick Littell, Akash Bharadwaj, Kartik Goyal, Chris Dyer, Lori Levin
Abstract
This paper contributes to a growing body of evidence that—when coupled with appropriate machine-learning techniques–linguistically motivated, information-rich representations can outperform one-hot encodings of linguistic data. In particular, we show that phonological features outperform character-based models. PanPhon is a database relating over 5,000 IPA segments to 21 subsegmental articulatory features. We show that this database boosts performance in various NER-related tasks. Phonologically aware, neural CRF models built on PanPhon features are able to perform better on monolingual Spanish and Turkish NER tasks that character-based models. They have also been shown to work well in transfer models (as between Uzbek and Turkish). PanPhon features also contribute measurably to Orthography-to-IPA conversion tasks.- Anthology ID:
- C16-1328
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 3475–3484
- Language:
- URL:
- https://aclanthology.org/C16-1328
- DOI:
- Cite (ACL):
- David R. Mortensen, Patrick Littell, Akash Bharadwaj, Kartik Goyal, Chris Dyer, and Lori Levin. 2016. PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3475–3484, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors (Mortensen et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/C16-1328.pdf
- Code
- dmort27/panphon