DocumentCode
3274950
Title
Text similarity computing based on sememe Vector Space
Author
Ke Zhang ; Jun Luo ; Xilin Chen
Author_Institution
Coll. of Comput., Chongqing Univ., Chongqing, China
fYear
2013
fDate
23-25 May 2013
Firstpage
208
Lastpage
211
Abstract
Vector Space Model (VSM) is a classic text presentation model in natural language processing. However the assumption that text terms are pairwise orthogonal is not suitable. General Vector Space Model (GVSM) was proposed to improve the VSM by using term similarity to overcome the pairwise orthogonal term assumption. In this paper, based on GVSM a new approach using HowNet sememe similarity to calculate text similarity in sememe space was proposed and verified by experiment.
Keywords
computational linguistics; natural language processing; text analysis; vectors; GVSM; HowNet sememe similarity; general vector space model; natural language processing; pairwise orthogonal term assumption; sememe space; sememe vector space; term similarity; text presentation model; text similarity computing; Information retrieval; GVSM; HowNet; VSM; orthogonal term; sememe similarity; text similarity;
fLanguage
English
Publisher
ieee
Conference_Titel
Software Engineering and Service Science (ICSESS), 2013 4th IEEE International Conference on
Conference_Location
Beijing
ISSN
2327-0586
Print_ISBN
978-1-4673-4997-0
Type
conf
DOI
10.1109/ICSESS.2013.6615289
Filename
6615289
Link To Document