Main verb identification is the task of automatically identifying the predicate-verb in a sentence. It is useful for many applications in Chinese Natural Language Processing. Although most studies have focused on the model used to identify the main verb, the definition of the main verb should not be overlooked. In our specification design, we have found many complicated issues that still need to be resolved since they haven’t been well discussed in previous works. Thus, the first novel aspect of our work is that we carefully design a specification for annotating
the main verb and investigate various complicated cases. We hope this discussion will help to uncover the difficulties involved in this problem. Secondly, we present an approach to realizing main verb identification based on the use of chunk information, which leads to better results than the approach based on part-of-speech. Finally, based on careful observation of the studied corpus, we propose new local and contextual features for main verb identification. According to our specification, we annotate a corpus and then use a Support Vector Machine
(SVM) to integrate all the features we propose. Our model, which was trained on our annotated corpus, achieved a promising F score of 92.8%. Furthermore, we show that main verb identification can improve the performance of the Chinese Sentence Breaker, one of the applications of main verb identification, by 2.4%.