Modeling language evolution with codes that utilize context and phonetic features

Javad Nouri; Roman Yangarber

Modeling language evolution with codes that utilize context and phonetic features

Avdelningen för datavetenskap

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

Sammanfattning

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.

Originalspråk	engelska
Titel på värdpublikation	The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL) : Proceedings of the Conference
Antal sidor	10
Utgivningsort	Stroudsburg, PA
Förlag	The Association for Computational Linguistics
Utgivningsdatum	2016
Sidor	136-145
ISBN (tryckt)	978-1-945626-19-7
Status	Publicerad - 2016
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	Conference on Computational Natural Language Learning - Berlin, Tyskland Varaktighet: 11 aug. 2016 → 12 aug. 2016 Konferensnummer: 20

Bibliografisk information

CoNLL 2016

Vetenskapsgrenar

113 Data- och informationsvetenskap

Åtkomst av dokument

http://aclweb.org/anthology/K/K16/K16-1014.pdf

Andra filer och länkar

Revita: Language learning and AI
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Projekt: Forskningsprojekt
LLL: Language Learning Lab
Yangarber, R., Katinskaia, A., Hou, J., Furlan, G. & Kylliäinen, I. P.
Projekt: Forskningsprojekt

Citera det här

@inproceedings{757ae4cf98644a65816c6cf0ffbcd526,

title = "Modeling language evolution with codes that utilize context and phonetic features",

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.",

keywords = "113 Computer and information sciences",

author = "Javad Nouri and Roman Yangarber",

note = "CoNLL 2016 ; Conference on Computational Natural Language Learning ; Conference date: 11-08-2016 Through 12-08-2016",

year = "2016",

language = "English",

isbn = "978-1-945626-19-7",

pages = "136--145",

booktitle = "The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL)",

publisher = "The Association for Computational Linguistics",

address = "United States",

}

Nouri, J & Yangarber, R 2016, Modeling language evolution with codes that utilize context and phonetic features. i The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL): Proceedings of the Conference. The Association for Computational Linguistics, Stroudsburg, PA, s. 136-145, Conference on Computational Natural Language Learning, Berlin, Tyskland, 11/08/2016. <http://aclweb.org/anthology/K/K16/K16-1014.pdf>

Modeling language evolution with codes that utilize context and phonetic features. / Nouri, Javad; Yangarber, Roman.

The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL): Proceedings of the Conference. Stroudsburg, PA : The Association for Computational Linguistics, 2016. s. 136-145.

Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

TY - GEN

T1 - Modeling language evolution with codes that utilize context and phonetic features

AU - Nouri, Javad

AU - Yangarber, Roman

N1 - Conference code: 20

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - 113 Computer and information sciences

UR - http://www.clips.uantwerpen.be/drupal7/sites/conll.org/files/program2016/185.html

UR - http://aclweb.org/anthology/K/K16/K16-1.pdf

M3 - Conference contribution

SN - 978-1-945626-19-7

SP - 136

EP - 145

BT - The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL)

PB - The Association for Computational Linguistics

CY - Stroudsburg, PA

T2 - Conference on Computational Natural Language Learning

Y2 - 11 August 2016 through 12 August 2016

ER -

Modeling language evolution with codes that utilize context and phonetic features

Sammanfattning

Bibliografisk information

Vetenskapsgrenar

Åtkomst av dokument

Andra filer och länkar

Projekt

Revita: Language learning and AI

LLL: Language Learning Lab

Citera det här