Lexical Representation and Classification of Eventive Verbs - Polarity and Interaction between Process and State

Event classification is one of the most crucial tasks in lexical semantic representation. Traditionally, researchers regarded process and state as two top level events and discriminated them by semantic and syntactic characteristics. In this paper, we add cause-result relativity as an auxiliary criterion to discriminate between process and state by structuring about 40,000 Chinese verbs to the two correspondent event hierarchies in E-HowNet. All verbs are classified according to their semantic similarity with the corresponding conceptual types of the ontology. As a consequence, we discover deficiencies of the dichotomy approach and point out that any discrete event classification system is insufficient to make a clear cut classification for synonyms with slightly different semantic focuses. We then propose a solution to remedy the deficiencies of the dichotomy approach. For the process or state type mismatched verbs, their inherited semantic properties will be adjusted according to their POS and semantic expressions to preserve their true semantic and syntactic information. Furthermore, cause-result relations will be linked between corresponding processes and states to bridge the gaps of the dichotomy approach.