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

Tietojenkäsittelytieteen osasto

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

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.

Alkuperäiskieli	englanti
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 2016

Tieteenalat

113 Tietojenkäsittely- ja informaatiotieteet

Pääsy asiakirjaan

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

Muut tiedostot ja linkit

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

Siteeraa tätä

@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. julkaisussa The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL): Proceedings of the Conference. The Association for Computational Linguistics, Stroudsburg, PA, Sivut 136-145, Conference on Computational Natural Language Learning, Berlin, Saksa, 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.

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussa › Konferenssiartikkeli › Tieteellinen › vertaisarvioitu

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

Abstrakti

Lisätietoja

Tieteenalat

Pääsy asiakirjaan

Muut tiedostot ja linkit

Projektit

Revita: Language learning and AI

LLL: Language Learning Lab

Siteeraa tätä