Abstract
Verb-particle combinations (VPCs) con- sist of a verbal and a preposition/particle component, which often have some addi- tional meaning compared to the meaning of their parts. If a data-driven morpholog- ical parser or a syntactic parser is trained on a dataset annotated with extra informa- tion for VPCs, they will be able to iden- tify VPCs in raw texts. In this paper, we examine how syntactic parsers perform on this task and we introduce VPCTag- ger, a machine learning-based tool that is able to identify English VPCs in context. Our method consists of two steps: it first selects VPC candidates on the basis of syntactic information and then selects gen- uine VPCs among them by exploiting new features like semantic and contextual ones. Based on our results, we see that VPC- Tagger outperforms state-of-the-art meth- ods in the VPC detection task.
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