POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process

Kairit Sirts; Jacob Eisenstein; Micha Elsner; Sharon Goldwater

doi:10.3115/v1/p14-2044

POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process

Kairit Sirts, Jacob Eisenstein, Micha Elsner, Sharon Goldwater

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

7 Citations (Scopus)

3 Downloads (Pure)

Abstract

We present a new approach to inducing the syntactic categories of words, combining their distributional and morphological properties in a joint nonparametric Bayesian model based on the distance-dependent Chinese Restaurant Process. The prior distribution over word clusterings uses a log-linear model of morphological similarity; the likelihood function is the probability of generating vector word embeddings. The weights of the morphology model are learned jointly while inducing part-of-speech clusters, encouraging them to cohere with the distributional features. The resulting algorithm outperforms competitive alternatives on English POS induction.

Original language	English
Title of host publication	52nd Annual Meeting of the Association for Computational Linguistics
Subtitle of host publication	proceedings of the conference : volume 2 : short papers
Place of Publication	Stroudsburg, PA
Publisher	Association for Computational Linguistics
Pages	265-271
Number of pages	7
Volume	2
ISBN (Print)	9781937284732
DOIs	https://doi.org/10.3115/v1/p14-2044
Publication status	Published - 2014
Externally published	Yes
Event	Annual meeting of the Association for Computational Linguistics (52nd : 2014) - Baltimore, USA Duration: 22 Jun 2014 → 27 Jun 2014

Conference

Conference	Annual meeting of the Association for Computational Linguistics (52nd : 2014)
City	Baltimore, USA
Period	22/06/14 → 27/06/14

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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10.3115/v1/p14-2044

Publisher version (open access)Final published version, 1.05 MBLicence: CC BY-NC-SA

http://acl2014.org/acl2014/index.html

Cite this

Sirts, K., Eisenstein, J., Elsner, M., & Goldwater, S. (2014). POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process. In 52nd Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : volume 2 : short papers (Vol. 2, pp. 265-271). Association for Computational Linguistics. https://doi.org/10.3115/v1/p14-2044

Sirts, Kairit ; Eisenstein, Jacob ; Elsner, Micha et al. / POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process. 52nd Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : volume 2 : short papers. Vol. 2 Stroudsburg, PA : Association for Computational Linguistics, 2014. pp. 265-271

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title = "POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process",

abstract = "We present a new approach to inducing the syntactic categories of words, combining their distributional and morphological properties in a joint nonparametric Bayesian model based on the distance-dependent Chinese Restaurant Process. The prior distribution over word clusterings uses a log-linear model of morphological similarity; the likelihood function is the probability of generating vector word embeddings. The weights of the morphology model are learned jointly while inducing part-of-speech clusters, encouraging them to cohere with the distributional features. The resulting algorithm outperforms competitive alternatives on English POS induction.",

author = "Kairit Sirts and Jacob Eisenstein and Micha Elsner and Sharon Goldwater",

note = "Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher. ; Annual meeting of the Association for Computational Linguistics (52nd : 2014) ; Conference date: 22-06-2014 Through 27-06-2014",

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doi = "10.3115/v1/p14-2044",

language = "English",

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Sirts, K, Eisenstein, J, Elsner, M & Goldwater, S 2014, POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process. in 52nd Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : volume 2 : short papers. vol. 2, Association for Computational Linguistics, Stroudsburg, PA, pp. 265-271, Annual meeting of the Association for Computational Linguistics (52nd : 2014), Baltimore, USA, 22/06/14. https://doi.org/10.3115/v1/p14-2044

POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process. / Sirts, Kairit; Eisenstein, Jacob; Elsner, Micha et al.
52nd Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : volume 2 : short papers. Vol. 2 Stroudsburg, PA: Association for Computational Linguistics, 2014. p. 265-271.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Goldwater, Sharon

N1 - Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2014

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N2 - We present a new approach to inducing the syntactic categories of words, combining their distributional and morphological properties in a joint nonparametric Bayesian model based on the distance-dependent Chinese Restaurant Process. The prior distribution over word clusterings uses a log-linear model of morphological similarity; the likelihood function is the probability of generating vector word embeddings. The weights of the morphology model are learned jointly while inducing part-of-speech clusters, encouraging them to cohere with the distributional features. The resulting algorithm outperforms competitive alternatives on English POS induction.

AB - We present a new approach to inducing the syntactic categories of words, combining their distributional and morphological properties in a joint nonparametric Bayesian model based on the distance-dependent Chinese Restaurant Process. The prior distribution over word clusterings uses a log-linear model of morphological similarity; the likelihood function is the probability of generating vector word embeddings. The weights of the morphology model are learned jointly while inducing part-of-speech clusters, encouraging them to cohere with the distributional features. The resulting algorithm outperforms competitive alternatives on English POS induction.

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DO - 10.3115/v1/p14-2044

M3 - Conference proceeding contribution

SN - 9781937284732

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SP - 265

EP - 271

BT - 52nd Annual Meeting of the Association for Computational Linguistics

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T2 - Annual meeting of the Association for Computational Linguistics (52nd : 2014)

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Sirts K, Eisenstein J, Elsner M, Goldwater S. POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process. In 52nd Annual Meeting of the Association for Computational Linguistics: proceedings of the conference : volume 2 : short papers. Vol. 2. Stroudsburg, PA: Association for Computational Linguistics. 2014. p. 265-271 doi: 10.3115/v1/p14-2044

POS induction with distributional and morphological information using a distance-dependent Chinese Restaurant Process

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