Abstract
Verbal Multi-Word Expressions (VMWEs) are very common in many languages. They include among other types the following types: Verb-Particle Constructions (VPC) (e.g. get around), Light-Verb Constructions (LVC) (e.g. make a decision), and idioms (ID) (e.g. break a leg). In this paper, we present a new dataset for supervised learning of VMWEs written in Yiddish. The dataset was manually collected and annotated from a web resource. It contains a set of positive examples for VMWEs and a set of non-VMWEs examples. While the dataset can be used for training supervised algorithms, the positive examples can be used as seeds in unsupervised bootstrapping algorithms. Moreover, we analyze the lexical properties of VMWEs written in Yiddish by classifying them to six categories: VPC, LVC, ID, Inherently Pronominal Verb (IPronV), Inherently Prepositional Verb (IPrepV), and other (OTH). The analysis suggests some interesting features of VMWEs for exploration. This dataset is a first step towards automatic identification of VMWEs written in Yiddish, which is important for natural language understanding, generation and translation systems.
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