Structural-fitting Word Vectors to Linguistic Ontology for Semantic Relatedness Measurement

Yang-Yin Lee, Ting-Yu Yen, Hen-Hsen Huang, Hsin-Hsi Chen

Abstract: With the aid of recently proposed word embedding algorithms, the study of semantic relatedness has progressed and advanced rapidly. In this research, we propose a novel structural-fitting method that utilizes the linguistic ontology into vector space representations. The ontological information is applied in two ways. The fine2coarse approach refines the word vectors from fine-grained to coarse-grained terms (word types), while the coarse2fine approach refines the word vectors from coarse-grained to fine-grained terms. In the experiments, we show that our proposed methods outperform previous approaches in seven publicly available benchmark datasets.