Word Similarity Computation Model of Multi-features Combination
Peiyin Zhang · Computer Technology and Development · 2014
Semantic similarity computing has been widely used in machine translation based on example,information retrieval and automatic question answering systems. Word similarity computation is generally based on the original in HowNet,through calculating the degree of similarity between concepts to obtain. In this paper,in consideration of the original distance,depth,width,density and contact ratio,use the method with multi- features to compute word similarity. In order to verify the rationality of the algorithm,using the benchmark of words given by M iller and Charles literature as a test set,make a comparison between the word similarity computation values and expert value,calculating the Pearson correlation coefficient,the calculation results is 0. 852. Experimental result showthat the word similarity computation of multi- features combination is identical with expert estimation.