Abstrakti
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.
Alkuperäiskieli | englanti |
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Otsikko | The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL) : Proceedings of the Conference |
Sivumäärä | 10 |
Julkaisupaikka | Stroudsburg, PA |
Kustantaja | The Association for Computational Linguistics |
Julkaisupäivä | 2016 |
Sivut | 136-145 |
ISBN (painettu) | 978-1-945626-19-7 |
Tila | Julkaistu - 2016 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
Tapahtuma | Conference on Computational Natural Language Learning - Berlin, Saksa Kesto: 11 elok. 2016 → 12 elok. 2016 Konferenssinumero: 20 |
Lisätietoja
CoNLL 2016Tieteenalat
- 113 Tietojenkäsittely- ja informaatiotieteet
Projektit
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Revita: Language learning and AI
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Projekti: Tutkimusprojekti
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LLL: Language Learning Lab
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Projekti: Tutkimusprojekti