Distinguishing Literal and Non-Literal Usage of German Particle Verbs

Abstract

This paper provides a binary, token-based classification of German particle verbs (PVs) into literal vs. non-literal usage. A random forest improving standard features (e.g., bagof-words; affective ratings) with PV-specific information and abstraction over common nouns significantly outperforms the majority baseline. In addition, PV-specific classification experiments demonstrate the role of shared particle semantics and semantically related base verbs in PV meaning shifts.

Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models

“…In a slightly different vein, non-literal word usage often correlates with the degree of abstractness of the word's contexts (Turney et al, 2011;Tsvetkov et al, 2014;Köper and Schulte im Walde, 2016). For example, the PV abschminken with the BV schminken 'to put on make-up' has a literal, very concrete meaning ('to remove makeup') and also a shifted, very abstract non-literal meaning ('to forget about something').…”

Proceedings of the Workshop on Figurative Language Processing

“…Thus abstractness ratings represent the de-gree of the abstractness of the thing the word refers to. Abstractness ratings have been shown as a determining factor for metaphor detection Dunn, 2013;Köper and im Walde, 2016).…”

Assessing Meaning Components in German Complex Verbs: A Collection of Source-Target Domains and Directionality

“…Some datasets include non-literal meanings of verbs (Birke and Sarkar, 2006;Turney et al, 2011;Shutova et al, 2013;Köper and Schulte im Walde, 2016b), and the MML-based meaning shift annotations by Lönneker-Rodman (2008) and Shutova and Teufel (2010) also include verbs but are less targetspecific than our work. In addition, while both Lönneker-Rodman (2008) and Shutova and Teufel (2010) asked their annotators to label words in their corpus data, we follow a different strategy and ask our participants to generate sentences according to domain-specific target senses.…”

“…For example, a series of formal-semantic analyses manually classified German PVs (with particles ab, an, auf, nach) into soft semantic classes (Lechler and Roßdeutscher, 2009;Haselbach, 2011;Kliche, 2011;Springorum, 2011). Corpus studies and annotations demonstrated the potential of German PVs to appear in non-literal language usage, and to trigger meaning shifts (Springorum et al, 2013;Köper and Schulte im Walde, 2016b). Regarding computational models, the majority of existing approaches to PV meaning addressed the automatic prediction of German PV compositionality (Salehi et al, 2014;Bott and Schulte im Walde, 2015;Köper and Schulte im Walde, 2017b), in a similar vein as computational approaches for English PVs (Baldwin et al, 2003;Bannard, 2005;McCarthy et al, 2003;Kim and Baldwin, 2007;Salehi and Cook, 2013;Salehi et al, 2014).…”