Abstract
We present methods for investigating processes of evolution in a language family by modeling relationships among the observed languages.
The models aim to find regularities---regular correspondences in lexical data. We present an algorithm which codes the data using phonetic features of sounds, and learns long-range contextual rules that condition recurrent sound correspondences between languages. This gives us a measure of model quality: better models find more regularity in the data. We also present a procedure for imputing unseen data, which provides another method of model comparison. Our experiments demonstrate improvements in performance compared to prior work.
The models aim to find regularities---regular correspondences in lexical data. We present an algorithm which codes the data using phonetic features of sounds, and learns long-range contextual rules that condition recurrent sound correspondences between languages. This gives us a measure of model quality: better models find more regularity in the data. We also present a procedure for imputing unseen data, which provides another method of model comparison. Our experiments demonstrate improvements in performance compared to prior work.
Original language | English |
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Title of host publication | The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL) : Proceedings of the Conference |
Number of pages | 10 |
Place of Publication | Stroudsburg, PA |
Publisher | The Association for Computational Linguistics |
Publication date | 2016 |
Pages | 136-145 |
ISBN (Print) | 978-1-945626-19-7 |
Publication status | Published - 2016 |
MoE publication type | A4 Article in conference proceedings |
Event | Conference on Computational Natural Language Learning - Berlin, Germany Duration: 11 Aug 2016 → 12 Aug 2016 Conference number: 20 |
Bibliographical note
CoNLL 2016Fields of Science
- 113 Computer and information sciences
Projects
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LLL: Language Learning Lab
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Project: Research project
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Revita: Language learning and AI
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Project: Research project