-
1
-
-
0002442796
-
Machine learning in automated text categorization
-
Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1) (2002) 1-47
-
(2002)
ACM Computing Surveys
, vol.34
, Issue.1
, pp. 1-47
-
-
Sebastiani, F.1
-
2
-
-
0000636553
-
Text categorization with Support Vector Machines: Learning with many relevant features
-
Chemnitz, DE
-
Joachims, T.: Text categorization with Support Vector Machines: learning with many relevant features. In: Proceedings of ECML, Chemnitz, DE (1998)
-
(1998)
Proceedings of ECML
-
-
Joachims, T.1
-
3
-
-
37148998691
-
-
Collins, M., Duffy, N.: Convolution kernels for natural language. In: NIPS, MIT Press (2001)
-
Collins, M., Duffy, N.: Convolution kernels for natural language. In: NIPS, MIT Press (2001)
-
-
-
-
4
-
-
34547975798
-
Efficient convolution kernels for dependency and constituent syntactic trees
-
Berlin, Germany
-
Moschitti, A.: Efficient convolution kernels for dependency and constituent syntactic trees. In: Proceedings of ECML, Berlin, Germany. (2006)
-
(2006)
Proceedings of ECML
-
-
Moschitti, A.1
-
5
-
-
0033699174
-
Support Vector Machines based on a semantic kernel for text categorization
-
Siolas, G., d'Alche Buc, F.: Support Vector Machines based on a semantic kernel for text categorization. In: IJCNN. Volume 5. (2000)
-
(2000)
IJCNN
, vol.5
-
-
Siolas, G.1
d'Alche Buc, F.2
-
6
-
-
37149050130
-
-
Mavroeidis, D., Tsatsaronis, G., Vazirgiannis, M., Theobald, M., Weikum, G.: Word sense disambiguation for exploiting hierarchical thesauri in text classification. In: PKDD. (2005)
-
Mavroeidis, D., Tsatsaronis, G., Vazirgiannis, M., Theobald, M., Weikum, G.: Word sense disambiguation for exploiting hierarchical thesauri in text classification. In: PKDD. (2005)
-
-
-
-
7
-
-
33748253466
-
Semantic kernels for text classification based on topological measures of feature similarity
-
Bloehdorn, S., Basili, R., Cammisa, M., Moschitti, A.: Semantic kernels for text classification based on topological measures of feature similarity. In: Proceedings of ICDM. (2006)
-
(2006)
Proceedings of ICDM
-
-
Bloehdorn, S.1
Basili, R.2
Cammisa, M.3
Moschitti, A.4
-
9
-
-
37149054274
-
-
Vapnik, V., Golowich, S.E., Smola, A.J.: Support vector method for function approximation, regression estimation and signal processing. In: NIPS. (1996)
-
Vapnik, V., Golowich, S.E., Smola, A.J.: Support vector method for function approximation, regression estimation and signal processing. In: NIPS. (1996)
-
-
-
-
11
-
-
0036498473
-
Latent Semantic Kernels
-
Cristianini, N., Shawe-Taylor, J., Lodhi, H.: Latent Semantic Kernels. Journal of Intelligent Information Systems 18(2-3) (2002) 127-152
-
(2002)
Journal of Intelligent Information Systems
, vol.18
, Issue.2-3
, pp. 127-152
-
-
Cristianini, N.1
Shawe-Taylor, J.2
Lodhi, H.3
-
14
-
-
85144480129
-
Dependency tree kernels for relation extraction
-
Culotta, A., Sorensen, J.: Dependency tree kernels for relation extraction. In: Proceedings of ACL. (2004)
-
(2004)
Proceedings of ACL
-
-
Culotta, A.1
Sorensen, J.2
-
16
-
-
85036466792
-
A study on convolution kernels for shallow semantic parsing
-
Moschitti, A.: A study on convolution kernels for shallow semantic parsing. In: proceedings of ACL. (2004)
-
(2004)
proceedings of ACL
-
-
Moschitti, A.1
-
17
-
-
33646760990
-
Evaluating wordnet-based measures of lexical semantic relatedness
-
Budanitsky, A., Hirst, G.: Evaluating wordnet-based measures of lexical semantic relatedness. Computational Linguistics 32(1) (2006) 13-47
-
(2006)
Computational Linguistics
, vol.32
, Issue.1
, pp. 13-47
-
-
Budanitsky, A.1
Hirst, G.2
-
18
-
-
1542377488
-
Question classification using Support Vector Machines
-
Zhang, D., Lee, W.S.: Question classification using Support Vector Machines. In: Proceedings of SIGIR. (2003)
-
(2003)
Proceedings of SIGIR
-
-
Zhang, D.1
Lee, W.S.2
-
19
-
-
0002714543
-
Making large-scale SVM learning practical
-
Joachims, T.: Making large-scale SVM learning practical. In: Advances in Kernel Methods. (1999)
-
(1999)
Advances in Kernel Methods
-
-
Joachims, T.1
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