Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pages 1422 - 1442 May 22-27, 2022 c⃝2022 Association for Computational Linguistics Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang Daphna Keidar ,∗ Andreas Opedal ,∗ Zhijing Jin , Mrinmaya Sachan ETH Zürich, Max Planck Institute for Intelligent Systems, Tübingen, Germany dkeidar@ethz.ch, andreas.opedal@inf.ethz.ch, zjin@tue.mpg.de, mrinmaya.sachan@inf.ethz.ch Abstract Languages are continuously undergoing changes, and the mechanisms that underlie these changes are still a matter of debate. In this work, we approach language evolution through the lens of causality in order to model not only how various distributional factors associate with language change, but how they causally affect it. In particular, we study slang, which is an informal language that is typically restricted to a specific group or social setting. We analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang words. With causal discovery and causal inference techniques, we measure the effect that word type (slang/nonslang) has on both semantic change and frequency shift, as well as its relationship to frequency, polysemy and part of speech. Our analysis provides some new insights in the study of language change, e.g., we show that slang words undergo less semantic change but tend to have larger frequency shifts over time.1 1 Introduction Language is a continuously evolving system, con- stantly resculptured by its speakers. The forces that drive this evolution are many, ranging from pho- netic convenience to sociocultural changes (Blank, 1999). In particular, the meanings of words and the frequencies in which they are used are not static, but rather evolve over time. Several pre- vious works, in both historical and computational linguistics, have described diachronic mechanisms, often suggesting causal relationships. For example, semantic change, i.e. change in the meaning of a word, has both been suggested to cause (Wilkins, 1993; Hopper and Traugott, 2003) and be caused by (Hamilton et al., 2016) polysemy, while also part ∗Equal contribution. 1Our code, along with the data, is made available at https://github.com/andreasopedal/slangvolution. Figure 1: We observe very different change dynamics for the slang word “duckface” and the nonslang word “inclusive.” “Inclusive” has acquired a new meaning, reflected in a high semantic change score of 0.77 as measured by our model. “Duckface” undergoes little semantic change, scored 0.39 by our model, while its usage frequency varies greatly. of speech (POS) has been implied to be a causal factor behind semantic change (Dubossarsky et al., 2016). However, none of these studies perform a causal analysis to verify these claims. Causality (Pearl, 2009) allows us to not only infer causal ef- fects between pairs of variables, but also model their interactions with other related factors. In this work, we focus on the linguistic evolution of slang, defined as colloquial and informal lan- guage commonly associated with particular groups (González, 1998; Bembe and Beukes, 2007), and use a causal framework to compare the change dynamics of slang words to those of standard …

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