A novel model for semantic similarity measurement based on wordnet and word embedding

被引:5
作者
Zhao, Fuqiang [1 ]
Zhu, Zhengyu [1 ]
Han, Ping [2 ]
机构
[1] Chongqing Univ, Coll Comp Sci, Chongqing, Peoples R China
[2] Chongqing Univ, Sch Foreign Languages & Cultures, Chongqing, Peoples R China
关键词
Semantic similarity; WordNet; word embedding; POS; synset;
D O I
10.3233/JIFS-202337
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To measure semantic similarity between words, a novel model DFRVec that encodes multiple semantic information of a word in WordNet into a vector space is presented in this paper. Firstly, three different sub-models are proposed: 1) DefVec: encoding the definitions of a word in WordNet; 2) FormVec: encoding the part-of-speech (POS) of a word in WordNet; 3) RelVec: encoding the relations of a word in WordNet. Then by combining the three sub-models with an existing word embedding, the new model for generating the vector of a word is proposed. Finally, based on DFRVec and the path information in WordNet, a new method DFRVec+Path to measure semantic similarity between words is presented. The experiments on ten benchmark datasets show that DFRVec+Path can outperform many existing methods on semantic similarity measurement.
引用
收藏
页码:9831 / 9842
页数:12
相关论文
共 37 条
[1]  
Abdeddaim S., 2018, MESH GRAM NEURAL NET
[2]  
Adhikari A., 2018, INTRINSIC INFORM CON
[3]  
Agirre E., 2009, P HUM LANG TECHN 200, P19, DOI DOI 10.3115/1620754.1620758
[4]  
Aouicha M.B., 2016, COMPUTING SEMANTIC S
[5]  
Araque O., 2019, SEMANTIC SIMILARITY
[6]  
Bruni Elia, 2014, MULTIMODAL DISTRIBUT
[7]  
Cai Y., 2018, HYBRID APPROACH MEAS
[8]  
Duong D., 2019, WORD SENTENCE EMBEDD
[9]  
Finkelstein L., 2002, PLACING SEARCH CONTE
[10]  
Gong C., 2018, P 32 INT C NEURAL IN, P1341