Abstract
German particle verbs, like anblicken (to gaze at) combine a base verb (blicken) with a particle (an) to form a special kind of Multi Word Expression. Particle verbs may share the semantics of the base verb and the particle to a variable degree. However, while syntactic subcategorization frames tend to be good predictor for the semantics of verbs in general (verbs that are similar in meaning also tend to have similar subcategorization frames and selectional preferences), there are regular changes in subcategorization frames by particle verbs with regard to the corresponding base verbs. This paper demonstrates that the syntactic behavior of particle verbs and base verbs together (modeling regular changes in subcategorization frames by particle verbs and corresponding base verbs) and applying clustering techniques allows us to distinguish particle verb meaning and shows the tight connection between transfer patterns and the semantic classes of particle verbs.
Highlights
In German, particle verbs (PVs), like anblicken in (1), are a highly productive class
We show that automatic clustering can replicate a gold standard classification of PV-BV pairs to a large degree when clustering only relies on syntax and the gold standard reflects semantic regularities
Since Latent Semantic Classes (LSC) is a soft clustering algorithm, there is a tradeoff between coverage and accuracy which depends on the cutoff point selected for the conversion into hard clusters
Summary
In German, particle verbs (PVs), like anblicken in (1), are a highly productive class. PVs present challenges for a both theoretical analysis and their computational treatment. One of the central problems is the prediction of their meaning from their constituent parts: the base verb (BV, e.g. blicken in (1)) and the particle (e.g. an). Many PVs derive their meaning from the corresponding BVs – with a varying degree of transparency Page numbers and proceedings footer are added by the organisers.
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