Semantic kernels for text classification based on topological measures of feature similarity

Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti

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

39 Citations (Scopus)

Abstract

In this paper we propose a new approach to the design of semantic smoothing kernels for text classification. These kernels implicitly encode a superconcept expansion in a semantic network using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations of little training data and data sparseness.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages808-812
Number of pages5
DOIs
Publication statusPublished - 1 Dec 2006
Externally publishedYes
Event6th International Conference on Data Mining, ICDM 2006 - Hong Kong, China
Duration: 18 Dec 200622 Dec 2006

Other

Other6th International Conference on Data Mining, ICDM 2006
CountryChina
CityHong Kong
Period18/12/0622/12/06

Fingerprint

Semantics

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Bloehdorn, S., Basili, R., Cammisa, M., & Moschitti, A. (2006). Semantic kernels for text classification based on topological measures of feature similarity. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 808-812). [4053107] https://doi.org/10.1109/ICDM.2006.141

Semantic kernels for text classification based on topological measures of feature similarity. / Bloehdorn, Stephan; Basili, Roberto; Cammisa, Marco; Moschitti, Alessandro.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 808-812 4053107.

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

Bloehdorn, S, Basili, R, Cammisa, M & Moschitti, A 2006, Semantic kernels for text classification based on topological measures of feature similarity. in Proceedings - IEEE International Conference on Data Mining, ICDM., 4053107, pp. 808-812, 6th International Conference on Data Mining, ICDM 2006, Hong Kong, China, 18/12/06. https://doi.org/10.1109/ICDM.2006.141
Bloehdorn S, Basili R, Cammisa M, Moschitti A. Semantic kernels for text classification based on topological measures of feature similarity. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. p. 808-812. 4053107 https://doi.org/10.1109/ICDM.2006.141
Bloehdorn, Stephan ; Basili, Roberto ; Cammisa, Marco ; Moschitti, Alessandro. / Semantic kernels for text classification based on topological measures of feature similarity. Proceedings - IEEE International Conference on Data Mining, ICDM. 2006. pp. 808-812
@inproceedings{ba69a123fc7941f4877add785726a6e7,
title = "Semantic kernels for text classification based on topological measures of feature similarity",
abstract = "In this paper we propose a new approach to the design of semantic smoothing kernels for text classification. These kernels implicitly encode a superconcept expansion in a semantic network using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations of little training data and data sparseness.",
author = "Stephan Bloehdorn and Roberto Basili and Marco Cammisa and Alessandro Moschitti",
year = "2006",
month = "12",
day = "1",
doi = "10.1109/ICDM.2006.141",
language = "English",
isbn = "0769527019",
pages = "808--812",
booktitle = "Proceedings - IEEE International Conference on Data Mining, ICDM",

}

TY - GEN

T1 - Semantic kernels for text classification based on topological measures of feature similarity

AU - Bloehdorn, Stephan

AU - Basili, Roberto

AU - Cammisa, Marco

AU - Moschitti, Alessandro

PY - 2006/12/1

Y1 - 2006/12/1

N2 - In this paper we propose a new approach to the design of semantic smoothing kernels for text classification. These kernels implicitly encode a superconcept expansion in a semantic network using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations of little training data and data sparseness.

AB - In this paper we propose a new approach to the design of semantic smoothing kernels for text classification. These kernels implicitly encode a superconcept expansion in a semantic network using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations of little training data and data sparseness.

UR - http://www.scopus.com/inward/record.url?scp=77956237073&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956237073&partnerID=8YFLogxK

U2 - 10.1109/ICDM.2006.141

DO - 10.1109/ICDM.2006.141

M3 - Conference contribution

AN - SCOPUS:77956237073

SN - 0769527019

SN - 9780769527017

SP - 808

EP - 812

BT - Proceedings - IEEE International Conference on Data Mining, ICDM

ER -