Annotation of WordNet Verbs with TimeML Event Classes

Abstract

This paper reports on the annotation of all English verbs included in WordNet 2.0 with TimeML event classes. Two annotators assign each verb present in WordNet the most relevant event class capturing most of that verb’s meanings. At the end of the annotation process, inter-annotator agreement is measured using kappa statistics, yielding a kappa value of 0.87. The cases of disagreement between the two independent annotations are clarified by obtaining a third, and in some cases, a fourth opinion, and finally each of the 11,306 WordNet verbs is mapped to a unique event class. The resulted annotation is then employed to automatically assign the corresponding class to each occurrence of a finite or non-finite verb in a given text. The evaluation performed on TimeBank reveals an F-measure of 86.43% achieved for the identification of verbal events, and an accuracy of 85.25% in the task of classifying them into TimeML event classes.

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