A Collocation-based Method for Semantic Similarity Measure for Chinese Words
Xia Fei · Zhongwen xinxi xuebao · 2013
The word similarity measure plays a basic role in many NLP related applications.In this paper,we propose a novel and practical method for this purpose with acceptable precision.Guided by the classic distribution hypothesis that words occur in similar contexts,we suggest the collocations in two-word noun phrases can serve as better contexts than the adjacent words because the former are more semantic related.By using automatic built large-scale noun phrases,we firstly construct tf-idf weighted words vectors containing direct and indirect collocations,and then take their cosine distances as desired semantic similarities.In order to compare with related approaches,we manually design a benchmark test set.On the benchmark test set,the proposed method achieves the correlation coefficients of 0.703,0.509,and 0.700 on nouns,verbs,and adjectives,respectively,at a coverage 100%.