Classification of Verb Particle Constructions with the Google Web1T Corpus

Manually maintaining comprehensive databases of multi-word expressions, for example Verb-Particle Constructions (VPCs), is infeasible. We describe a new type level classifier for potential VPCs, which uses information in the Google Web1T corpus to perform a simple linguistic constituency test. Specifically, we consider the fronting test, comparing the frequencies of the two possible orderings of the given verb and particle. Using only a small set of queries for each verb-particle pair, the system was able to achieve an F-score of 75.7% in our evaluation while processing thousands of queries a second.

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