Metaphor Analysis Method Based on Latent Semantic Analysis

摘要

Current research on metaphor analysis is generally knowledge-based and corpus-based,which calls for methods of automatic feature extraction and weight calculation.Combining natural language processing(NLP),latent semantic analysis(LSA),and Pearson correlation coefficient,this paper proposes a metaphor analysis method for extracting the content words from both literal and metaphorical corpus,calculating correlation degree,and analyzing their relationships.The value of the proposed method was demonstrated through a case study by using a corpus with keyword“飞翔(fly)”.When compared with the method of Pearson correlation coefficient,the experiment shows that the LSA can produce better results with greater significance in correlation degree.It is also found that the number of common words that appeared in both literal and metaphorical word bags decreased with the correlation degree.The case study also revealed that there are more nouns appear in literal corpus,and more adjectives and adverbs appear in metaphorical corpus.The method proposed will benefit NLP researchers to develop the required step-by-step calculation tools for accurate quantitative analysis.