Deriving Probabilistic Semantic Frames from HowNet

Abstract

Representing knowledge as frames has a long history in artificial intelligence and computational linguistics. However, constructing frame banks that support frame-based processing is quite time-consuming, leading to the unavailability of usable frame banks for many languages, including Chinese. This paper proposed a method for deriving probabilistic semantic frames from HowNet, which is a well-known common-sense knowledge base and has been successfully used in many NLP applications. Unlike most previous HowNet-related work which focused on using HowNet as a lexico-semantic bank, this work viewed the HowNet dictionary as a semantic-annotated corpus. According to the proposed method, governor-role-dependent triples are firstly extracted from the concept definitions of the HowNet dictionary. Then, they are organized into frames by the governors and the probabilities are estimated based on maximum likelihood estimation (MLE). Finally, the probabilistic frames form a frame bank. Moreover, in order to overcome the data sparseness problem, a smoothing method based on HowNet’s taxonomy was put forward. To verify the constructed frame bank, we applied it in a task to recognize relationships between Chinese word pairs, which are extracted from the Chinese Message Structure Database of HowNet. The experimental results showed that, even without using context information, the system based on the constructed frame bank achieved an accuracy of 83.74%, which indicates the soundness of the constructed frame bank.