Presentation	1998/10/16 Automatic acquisition of semantic representations with neural networks Naoto TAKAHASHI,
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Abstract(in English)	Real-valued vectors are useful for representing the meanings of words. This paper describes a method for acquiring such vectors from a corpus by using a neural network. We modified Miikkulainen's FGREP so that the neural network outputs case-roles instead of word representations ; this modification decreased the number of necessary units and increased the speed of learning. The acquired semantic representations showed a distribution that reflects the usage of each word in the corpus. We also confirmed the generalisation ability of our network with cross validation tests. Finally, we present ideas to extend our method to syntactic categories higher than words.
Keyword(in Japanese)	(See Japanese page)
Keyword(in English)	semantic representation / corpus based / neural network / FGREP
Paper #	NLC98-28
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Conference Information
Committee	NLC
Conference Date	1998/10/16(1days)
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Paper Information
Registration To	Natural Language Understanding and Models of Communication (NLC)
Language	JPN
Title (in Japanese)	(See Japanese page)
Sub Title (in Japanese)	(See Japanese page)
Title (in English)	Automatic acquisition of semantic representations with neural networks
Sub Title (in English)
Keyword(1)	semantic representation
Keyword(2)	corpus based
Keyword(3)	neural network
Keyword(4)	FGREP
1st Author's Name	Naoto TAKAHASHI
1st Author's Affiliation	Machine Understanding Division Electotechnical Laboratory()
Date	1998/10/16
Paper #	NLC98-28
Volume (vol)	vol.98
Number (no)	338
Page	pp.pp.-
#Pages	8
Date of Issue