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

Abstract

We present a computational model to detect and distinguish analogies in meaning shifts between German base and complex verbs. In contrast to previous corpus-based studies, a novel dataset demonstrates that "regular" shifts represent the smallest class. Classification experiments relying on a standard similarity model successfully distinguish between four types of shifts, with verb classes boosting the performance, and affective features for abstractness, emotion and sentiment representing the most salient indicators.

German particle verbs: Compositionality at the syntax-semantics interface

“…Often, similar semantic derivations apply to semantically similar BVs, such as schneiden and sägen in examples (4) and (5), which both refer to a cutting event. In these cases, we find regular semantic shifts, where combining semantically similar BVs with specific particle types results in semantically similar PVs (Springorum et al 2013b;Köper and Schulte im Walde 2018). We refer to these regular semantic shifts as semantic transfer patterns.…”

Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality

“…In NLP, informing models with emotionality features has also proven to be useful for metaphor detection. While Gargett and Barnden (2015) and Köper and Schulte im Walde (2018) showed that emotion features are improving performance in distinguishing metaphorical vs. literal usages, Dankers et al (2019) found that dominance (the perceived degree of control in a social situation) also improved considerably their BERT model to identify the use of metaphors. Role of Context for Metaphorical Language On the one hand, many words are ambiguous, and the empirical study provided by Mohammad et al (2016) confirmed the hypothesis that variation in metaphorical and literal language usage is a common pattern for polysemous verbs.…”

“…The vast majority of work in NLP has been concerned with the detection of metaphorical expres-sions 1 . Linguistically and conceptually-driven strategies involve identifying selectional preference violations (compare consume food vs. consume information) (Fazly et al, 2009;Shutova et al, 2013;Ehren et al, 2020), judging discourse coherence (Sporleder and Li, 2009;Bogdanova, 2010;Dankers et al, 2020), and inducing discourse features indicating figurative language, such as supersenses, concreteness, emotionalty, imageability (Turney et al, 2011;Tsvetkov et al, 2013;Köper and Schulte im Walde, 2016b;Mohammad et al, 2016;Köper and Schulte im Walde, 2018;Alnafesah et al, 2020;Hall Maudslay et al, 2020). Existing research has however focused solely on sentences with word-level and phrase-level metaphorical expressions, and there has been little discussion on metaphoricity on the discourse level.…”

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

“…As to our knowledge, there is no previous dataset on meaning shifts of complex verbs, other than a smaller-scale collection developed in parallel by ourselves, which however focuses on analogies in meaning shifts rather than source-target domains (Köper and Schulte im Walde, 2018). 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.…”