Verb-particle combinations (VPCs) consist of a verbal and a preposition/particle component, which often have some additional meaning compared to the meaning of their parts. If a data-driven morphological parser or a syntactic parser is trained on a dataset annotated with extra information for VPCs, they will be able to identify VPCs in raw texts. In this paper, we examine how syntactic parsers perform on this task and we introduce VPCTagger, 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 genuine VPCs among them by exploiting new features like semantic and contextual ones. Based on our results, we see that VPCTagger outperforms state-of-the-art methods in the VPC detection task.
REF implemented VPCTagger, a machine learning-based tool that selects VPC candidates on the basis of syntactic information and then classifies them as VPCs or not, based on lexical, syntactic and semantic features.
VPCTagger: Detecting Verb-Particle Constructions With Syntax-Based Methods
MWE@EACL
Computer Science