SVD and clustering for unsupervised POS tagging

Michael Lamar; Yariv Maron; Mark Johnson; Elie Bienenstock

SVD and clustering for unsupervised POS tagging

Michael Lamar^*, Yariv Maron, Mark Johnson, Elie Bienenstock

^*Corresponding author for this work

School of Computing

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

20 Citations (Scopus)

Abstract

We revisit the algorithm of Schutze (1995) for unsupervised part-of-speech tagging. The algorithm uses reduced-rank singular value decomposition followed by clustering to extract latent features from context distributions. As implemented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also produce a range of finer-grained taggings, with potential applications to various tasks.

Original language	English
Title of host publication	ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Place of Publication	Stroudsburg, PA
Publisher	Association for Computational Linguistics (ACL)
Pages	215-219
Number of pages	5
ISBN (Print)	9781617388088
Publication status	Published - 2010
Event	48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Uppsala, Sweden Duration: 11 Jul 2010 → 16 Jul 2010

Other

Other	48th Annual Meeting of the Association for Computational Linguistics, ACL 2010
Country/Territory	Sweden
City	Uppsala
Period	11/07/10 → 16/07/10

Cite this

@inproceedings{49a57b8c8ccf44be931dff168a9a9b4e,

title = "SVD and clustering for unsupervised POS tagging",

abstract = "We revisit the algorithm of Schutze (1995) for unsupervised part-of-speech tagging. The algorithm uses reduced-rank singular value decomposition followed by clustering to extract latent features from context distributions. As implemented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also produce a range of finer-grained taggings, with potential applications to various tasks.",

author = "Michael Lamar and Yariv Maron and Mark Johnson and Elie Bienenstock",

year = "2010",

language = "English",

isbn = "9781617388088",

pages = "215--219",

booktitle = "ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",

publisher = "Association for Computational Linguistics (ACL)",

note = "48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 ; Conference date: 11-07-2010 Through 16-07-2010",

}

Lamar, M, Maron, Y, Johnson, M & Bienenstock, E 2010, SVD and clustering for unsupervised POS tagging. in ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), Stroudsburg, PA, pp. 215-219, 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010, Uppsala, Sweden, 11/07/10.

SVD and clustering for unsupervised POS tagging. / Lamar, Michael; Maron, Yariv; Johnson, Mark et al.
ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Stroudsburg, PA: Association for Computational Linguistics (ACL), 2010. p. 215-219.

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

TY - GEN

T1 - SVD and clustering for unsupervised POS tagging

AU - Lamar, Michael

AU - Maron, Yariv

AU - Johnson, Mark

AU - Bienenstock, Elie

PY - 2010

Y1 - 2010

N2 - We revisit the algorithm of Schutze (1995) for unsupervised part-of-speech tagging. The algorithm uses reduced-rank singular value decomposition followed by clustering to extract latent features from context distributions. As implemented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also produce a range of finer-grained taggings, with potential applications to various tasks.

AB - We revisit the algorithm of Schutze (1995) for unsupervised part-of-speech tagging. The algorithm uses reduced-rank singular value decomposition followed by clustering to extract latent features from context distributions. As implemented here, it achieves state-of-the-art tagging accuracy at considerably less cost than more recent methods. It can also produce a range of finer-grained taggings, with potential applications to various tasks.

UR - http://www.scopus.com/inward/record.url?scp=84859978666&partnerID=8YFLogxK

M3 - Conference proceeding contribution

AN - SCOPUS:84859978666

SN - 9781617388088

SP - 215

EP - 219

BT - ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference

PB - Association for Computational Linguistics (ACL)

CY - Stroudsburg, PA

T2 - 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010

Y2 - 11 July 2010 through 16 July 2010

ER -

SVD and clustering for unsupervised POS tagging

Abstract

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