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
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;
Conference_Titel :
Software Engineering and Service Science (ICSESS), 2013 4th IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-4997-0
DOI :
10.1109/ICSESS.2013.6615289
