Collocation extraction systems based on pure statistical methods suffer from two major problems. The first problem is their relatively low precision and recall rates. The second problem is their difficulty in dealing with sparse collocations. In order to improve performance, both statistical and lexicographic approaches should be considered. This paper presents a new method to extract synonymous collocations using semantic information. The semantic information is obtained by calculating similarities from HowNet. We have successfully extracted synonymous
collocations which normally cannot be extracted using lexical statistics. Our evaluation conducted on a 60MB tagged corpus shows that we can extract
synonymous collocations that occur with very low frequency and that the
improvement in the recall rate is close to 100%. In addition, compared with a collocation extraction system based on the Xtract system for English, our algorithm can improve the precision rate by about 44%.