Structure and semantics for expressive text kernels

Stephan Bloehdorn, Alessandro Moschitti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Citations (Scopus)

Abstract

Several Text Categorization applications require a representation beyond the standard bag-of-words paradigm. Kernel-based learning has approached this problem by (i) considering information about syntactic structure or by (ii) incorporating knowledge about the semantic similarity of term features. We propose a generalized framework consisting of a family of kernels that jointly incorporate syntactic and semantic similarity and demonstrate the power of this approach in a series of experiments.

Original languageEnglish
Title of host publicationInternational Conference on Information and Knowledge Management, Proceedings
Pages861-864
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2007
Externally publishedYes
Event16th ACM Conference on Information and Knowledge Management, CIKM 2007 - Lisboa, Portugal
Duration: 6 Nov 20079 Nov 2007

Other

Other16th ACM Conference on Information and Knowledge Management, CIKM 2007
CountryPortugal
CityLisboa
Period6/11/079/11/07

Fingerprint

Kernel
Semantic similarity
Text categorization
Paradigm
Experiment

Keywords

  • Kernel Methods
  • Machine Learning
  • Text Classification

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Bloehdorn, S., & Moschitti, A. (2007). Structure and semantics for expressive text kernels. In International Conference on Information and Knowledge Management, Proceedings (pp. 861-864) https://doi.org/10.1145/1321440.1321561

Structure and semantics for expressive text kernels. / Bloehdorn, Stephan; Moschitti, Alessandro.

International Conference on Information and Knowledge Management, Proceedings. 2007. p. 861-864.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Bloehdorn, S & Moschitti, A 2007, Structure and semantics for expressive text kernels. in International Conference on Information and Knowledge Management, Proceedings. pp. 861-864, 16th ACM Conference on Information and Knowledge Management, CIKM 2007, Lisboa, Portugal, 6/11/07. https://doi.org/10.1145/1321440.1321561
Bloehdorn S, Moschitti A. Structure and semantics for expressive text kernels. In International Conference on Information and Knowledge Management, Proceedings. 2007. p. 861-864 https://doi.org/10.1145/1321440.1321561
Bloehdorn, Stephan ; Moschitti, Alessandro. / Structure and semantics for expressive text kernels. International Conference on Information and Knowledge Management, Proceedings. 2007. pp. 861-864
@inproceedings{ee40fe28bfeb43f2afd8f1b7126d6fc1,
title = "Structure and semantics for expressive text kernels",
abstract = "Several Text Categorization applications require a representation beyond the standard bag-of-words paradigm. Kernel-based learning has approached this problem by (i) considering information about syntactic structure or by (ii) incorporating knowledge about the semantic similarity of term features. We propose a generalized framework consisting of a family of kernels that jointly incorporate syntactic and semantic similarity and demonstrate the power of this approach in a series of experiments.",
keywords = "Kernel Methods, Machine Learning, Text Classification",
author = "Stephan Bloehdorn and Alessandro Moschitti",
year = "2007",
month = "12",
day = "1",
doi = "10.1145/1321440.1321561",
language = "English",
isbn = "9781595938039",
pages = "861--864",
booktitle = "International Conference on Information and Knowledge Management, Proceedings",

}

TY - GEN

T1 - Structure and semantics for expressive text kernels

AU - Bloehdorn, Stephan

AU - Moschitti, Alessandro

PY - 2007/12/1

Y1 - 2007/12/1

N2 - Several Text Categorization applications require a representation beyond the standard bag-of-words paradigm. Kernel-based learning has approached this problem by (i) considering information about syntactic structure or by (ii) incorporating knowledge about the semantic similarity of term features. We propose a generalized framework consisting of a family of kernels that jointly incorporate syntactic and semantic similarity and demonstrate the power of this approach in a series of experiments.

AB - Several Text Categorization applications require a representation beyond the standard bag-of-words paradigm. Kernel-based learning has approached this problem by (i) considering information about syntactic structure or by (ii) incorporating knowledge about the semantic similarity of term features. We propose a generalized framework consisting of a family of kernels that jointly incorporate syntactic and semantic similarity and demonstrate the power of this approach in a series of experiments.

KW - Kernel Methods

KW - Machine Learning

KW - Text Classification

UR - http://www.scopus.com/inward/record.url?scp=63449104310&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=63449104310&partnerID=8YFLogxK

U2 - 10.1145/1321440.1321561

DO - 10.1145/1321440.1321561

M3 - Conference contribution

SN - 9781595938039

SP - 861

EP - 864

BT - International Conference on Information and Knowledge Management, Proceedings

ER -