DocumentCode :
233636
Title :
Word clustering based on word2vec and semantic similarity
Author :
Luo Jie ; Wang Qinglin ; Li Yuan
Author_Institution :
Sch. of Autom., Beijing Inst. of Technol., Beijing, China
fYear :
2014
fDate :
28-30 July 2014
Firstpage :
517
Lastpage :
521
Abstract :
Domain words clustering have important theoretical and practical significance in text categorization, the ontology research, machine learning and many other research areas. The domain words clustering method in this article is a method based on word2vec and semantic similarity computation. First of all, we get the candidate word set with word2vec tools to preliminary clustering of words. Then we tectonic domain category semantic core word set and screening candidate word set by means of semantic similarity computation. Finally we get new word set belongs to the target domain and get the word set in the field of clustering. Experiments show that this method has higher recall ratio and accuracy.
Keywords :
learning (artificial intelligence); ontologies (artificial intelligence); pattern clustering; text analysis; Word2vec; candidate word set; domain words clustering method; machine learning; ontology research; screening candidate word set; semantic similarity computation; tectonic domain category semantic core word set; text categorization; Abstracts; Automation; Educational institutions; Electronic mail; Ontologies; Semantics; Text categorization; domain ontology; semantic similarity; word clustering; word2vec;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2014 33rd Chinese
Conference_Location :
Nanjing
Type :
conf
DOI :
10.1109/ChiCC.2014.6896677
Filename :
6896677
Link To Document :
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