Large Scale Book Annotation with Social Tags

Sharon Givon; Victor Lavrenko

Large Scale Book Annotation with Social Tags

Sharon Givon, Victor Lavrenko

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

Abstract

We describe work on large scale automatic annotation of full texts of books with social tags. Our task consisted of assigning tags to the full texts of works of fiction and evaluating them against tags assigned by humans. We compared Boosting and Relevance Models (RM) methods to explore how they differ primarily in terms scalability and also annotation quality. We extended beyond the set of 50 tags used in earlier work to sets ranging up to 10,000 tags. We show how a RM based algorithm scales significantly better than a Boosting based algorithm when dealing with large sets of tags.

Original language	English
Title of host publication	Proceedings of the Third International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, California, USA, May 17-20, 2009
Publisher	The AAAI Press
Pages	210-213
Number of pages	4
Publication status	Published - Mar 2009

Access to Document

http://aaai.org/ocs/index.php/ICWSM/09/paper/view/198

Cite this

@inproceedings{6db2aa8656a6432e88f77585b2bcfaf9,

title = "Large Scale Book Annotation with Social Tags",

abstract = "We describe work on large scale automatic annotation of full texts of books with social tags. Our task consisted of assigning tags to the full texts of works of fiction and evaluating them against tags assigned by humans. We compared Boosting and Relevance Models (RM) methods to explore how they differ primarily in terms scalability and also annotation quality. We extended beyond the set of 50 tags used in earlier work to sets ranging up to 10,000 tags. We show how a RM based algorithm scales significantly better than a Boosting based algorithm when dealing with large sets of tags.",

author = "Sharon Givon and Victor Lavrenko",

year = "2009",

month = mar,

language = "English",

pages = "210--213",

booktitle = "Proceedings of the Third International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, California, USA, May 17-20, 2009",

publisher = "The AAAI Press",

}

TY - GEN

T1 - Large Scale Book Annotation with Social Tags

AU - Givon, Sharon

AU - Lavrenko, Victor

PY - 2009/3

Y1 - 2009/3

N2 - We describe work on large scale automatic annotation of full texts of books with social tags. Our task consisted of assigning tags to the full texts of works of fiction and evaluating them against tags assigned by humans. We compared Boosting and Relevance Models (RM) methods to explore how they differ primarily in terms scalability and also annotation quality. We extended beyond the set of 50 tags used in earlier work to sets ranging up to 10,000 tags. We show how a RM based algorithm scales significantly better than a Boosting based algorithm when dealing with large sets of tags.

AB - We describe work on large scale automatic annotation of full texts of books with social tags. Our task consisted of assigning tags to the full texts of works of fiction and evaluating them against tags assigned by humans. We compared Boosting and Relevance Models (RM) methods to explore how they differ primarily in terms scalability and also annotation quality. We extended beyond the set of 50 tags used in earlier work to sets ranging up to 10,000 tags. We show how a RM based algorithm scales significantly better than a Boosting based algorithm when dealing with large sets of tags.

M3 - Conference contribution

SP - 210

EP - 213

BT - Proceedings of the Third International Conference on Weblogs and Social Media, ICWSM 2009, San Jose, California, USA, May 17-20, 2009

PB - The AAAI Press

ER -

Large Scale Book Annotation with Social Tags

Abstract

Access to Document

Fingerprint

Cite this