Building the Chinese Open Wordnet (COW): Starting from Core Synsets

Princeton WordNet (PWN) is one of the most influential resources for semantic descriptions, and is extensively used in natural language processing. Based on PWN, three Chinese wordnets have been developed: Sinica Bilingual Ontological Wordnet (BOW), Southeast University WordNet (SEW), and Taiwan University WordNet (CWN). We used SEW to sense-tag a corpus, but found some issues with coverage and precision. We decided to make a new Chinese wordnet based on SEW to increase the coverage and accuracy. In addition, a small scale Chinese wordnet was constructed from open multilingual wordnet (OMW) using data from Wiktionary (WIKT). We then merged SEW and WIKT. Starting from core synsets, we formulated guidelines for the new Chinese Open Wordnet (COW). We compared the five Chinese wordnets, which shows that COW is currently the best, but it still has room for further improvement, especially with polysemous words. It is clear that building an accurate semantic resource for a language is not an easy task, but through consistent efforts, we will be able to achieve it. COW is released under the same license as the PWN, an open license that freely allows use, adaptation and redistribution.