The standard representation of text documents as bags of words suffers from well known limitations, mostly due to its inability to exploit semantic similarity between terms. Attempts to incorporate some notion of term similarity include latent semantic indexing 8, the use of semantic networks 9, and probabilistic methods 5. In this paper we propose two methods for inferring such similarity from a corpus. The first one defines word-similarity based on document-similarity and viceversa,...
%0 Conference Paper
%1 Kandola02
%A Kandola, Jaz S.
%A Shawe-Taylor, John
%A Cristianini, Nello
%B Advances in Neural Information Processing Systems 15: Neural
%D 2002
%E Becker, Suzanna
%E Thrun, Sebastian
%E Obermayer, Klaus
%I MIT Press
%K knowledge matching measures semantic-measure semantic-similarity semantics
%P 657-664
%T Learning Semantic Similarity.
%U http://arxiv.org/abs/1310.8059
%X The standard representation of text documents as bags of words suffers from well known limitations, mostly due to its inability to exploit semantic similarity between terms. Attempts to incorporate some notion of term similarity include latent semantic indexing 8, the use of semantic networks 9, and probabilistic methods 5. In this paper we propose two methods for inferring such similarity from a corpus. The first one defines word-similarity based on document-similarity and viceversa,...
%@ 0-262-02550-7
@inproceedings{Kandola02,
abstract = {The standard representation of text documents as bags of words suffers from well known limitations, mostly due to its inability to exploit semantic similarity between terms. Attempts to incorporate some notion of term similarity include latent semantic indexing [8], the use of semantic networks [9], and probabilistic methods [5]. In this paper we propose two methods for inferring such similarity from a corpus. The first one defines word-similarity based on document-similarity and viceversa,...},
added-at = {2018-12-19T13:31:32.000+0100},
author = {Kandola, Jaz S. and Shawe-Taylor, John and Cristianini, Nello},
bibsource = {DBLP, http://dblp.uni-trier.de},
biburl = {https://www.bibsonomy.org/bibtex/2773c9b4aea2fb9d479039ae409ac3fa8/theodoro},
booktitle = {Advances in Neural Information Processing Systems 15: Neural},
editor = {Becker, Suzanna and Thrun, Sebastian and Obermayer, Klaus},
interhash = {38e07eb87b57be59d0fff6aa13551312},
intrahash = {773c9b4aea2fb9d479039ae409ac3fa8},
isbn = {0-262-02550-7},
keywords = {knowledge matching measures semantic-measure semantic-similarity semantics},
pages = {657-664},
publisher = {MIT Press},
timestamp = {2018-12-19T13:31:32.000+0100},
title = {Learning Semantic Similarity.},
url = {http://arxiv.org/abs/1310.8059},
year = 2002
}