  Princeton WordNet (PWN) is a lexicon-semantic network based on cognitive
linguistics, which promotes the development of natural language processing.
Based on PWN, five Chinese wordnets have been developed to solve the problems
of syntax and semantics. They include: Northeastern University Chinese WordNet
(NEW), Sinica Bilingual Ontological WordNet (BOW), Southeast University Chinese
WordNet (SEW), Taiwan University Chinese WordNet (CWN), Chinese Open WordNet
(COW). By using them, we found that these word networks have low accuracy and
coverage, and cannot completely portray the semantic network of PWN. So we
decided to make a new Chinese wordnet called Multi-Fusion Chinese Wordnet (MCW)
to make up those shortcomings. The key idea is to extend the SEW with the help
of Oxford bilingual dictionary and Xinhua bilingual dictionary, and then
correct it. More specifically, we used machine learning and manual adjustment
in our corrections. Two standards were formulated to help our work. We
conducted experiments on three tasks including relatedness calculation, word
similarity and word sense disambiguation for the comparison of lemma's
accuracy, at the same time, coverage also was compared. The results indicate
that MCW can benefit from coverage and accuracy via our method. However, it
still has room for improvement, especially with lemmas. In the future, we will
continue to enhance the accuracy of MCW and expand the concepts in it.
