Impact of MWE resources on multiword recognition

Abstract

In this paper, we demonstrate the impact of Multiword Expression (MWE) resources in the task of MWE recognition in text. We present results based on the Wiki50 corpus for MWE resources, generated using unsupervised methods from raw text and resources that are extracted using manual text markup and lexical resources. We show that resources acquired from manual annotation yield the best MWE tagging performance. However, a more fine-grained analysis that differentiates MWEs according to their part of speech (POS) reveals that automatically acquired MWE lists outperform the resources generated from human knowledge for three out of four classes.