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        <title>PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors</title>
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        <namePart type="given">David</namePart>
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors
%A Mortensen, David R.
%A Littell, Patrick
%A Bharadwaj, Akash
%A Goyal, Kartik
%A Dyer, Chris
%A Levin, Lori
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F mortensen-etal-2016-panphon
%X 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.
%U https://aclanthology.org/C16-1328
%P 3475-3484

Markdown (Informal)

[PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors](https://aclanthology.org/C16-1328) (Mortensen et al., COLING 2016)

• PanPhon: A Resource for Mapping IPA Segments to Articulatory Feature Vectors (Mortensen et al., COLING 2016)

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.