splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications

Y. Goldberg; Michael Elhadad

splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications

Y. Goldberg, Michael Elhadad

CS@BIU

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

Abstract

We present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for NLP applications. We apply the method to the MaltParser system, resulting in a Java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). The method implementation is available as the open-source splitSVM Java library.

Original language	American English
Title of host publication	46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Publisher	Association for Computational Linguistics
State	Published - 2008

Bibliographical note

Place of conference:USA

Cite this

@inproceedings{dc195c0788c94846ae55be8b844ba795,

title = "splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications",

abstract = "We present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for NLP applications. We apply the method to the MaltParser system, resulting in a Java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). The method implementation is available as the open-source splitSVM Java library.",

author = "Y. Goldberg and Michael Elhadad",

note = "Place of conference:USA",

year = "2008",

language = "American English",

booktitle = "46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers",

publisher = "Association for Computational Linguistics",

}

splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications. / Goldberg, Y.; Elhadad, Michael.
46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers. Association for Computational Linguistics, 2008.

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

TY - GEN

T1 - splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications

AU - Goldberg, Y.

AU - Elhadad, Michael

N1 - Place of conference:USA

PY - 2008

Y1 - 2008

N2 - We present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for NLP applications. We apply the method to the MaltParser system, resulting in a Java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). The method implementation is available as the open-source splitSVM Java library.

AB - We present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for NLP applications. We apply the method to the MaltParser system, resulting in a Java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). The method implementation is available as the open-source splitSVM Java library.

UR - https://scholar.google.co.il/scholar?q=splitSVM%3A+Fast%2C+Space-Efficient%2C+non-Heuristic%2C+Polynomial+Kernel+Computation+for+NLP+Applications&btnG=&hl=en&as_sdt=0%2C5

M3 - Conference contribution

BT - 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers

PB - Association for Computational Linguistics

ER -

splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications

Abstract

Bibliographical note

Other files and links

Fingerprint

Cite this