Automatic Acquisition of Attribute Host by Selectional Constraint Resolution

摘要

It is well known that lexical knowledge sources such as WordNet, HowNet are very important to natural language processing applications. In those lexical resources, attributes play very important roles for defining and distinguishing different concepts. In this paper, we propose a novel method to automatically discover the attribute hosts of HowNet's attribute set. Given an attribute, we model the solving of its host as a problem of selectional constraint resolution. The World Wide Web is exploited as a large corpus to acquire the training data for such a model. From the training data, the attribute hosts are discovered by using a statistical measure and a semantic hierarchy. We evaluate our algorithm by comparing the result with the original hand-coded attribute specification in HowNet. Some experimental results about the performance of the method are provided.