Predicting the Growth of Morphological Families from Social and Linguistic Factors

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7273–7283 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 7273 Predicting the Growth of Morphological Families from Social and Linguistic Factors Valentin Hofmann*‡, Janet B. Pierrehumbert†*, Hinrich Sch¨utze‡ *Faculty of Linguistics, University of Oxford †Department of Engineering Science, University of Oxford ‡Center for Information and Language Processing, LMU Munich valentin.hofmann@ling-phil.ox.ac.uk Abstract We present the first study that examines the evolution of morphological families, i.e., sets of morphologically related words such as “trump”, “antitrumpism”, and “detrumpify”, in social media. We introduce the novel task of Morphological Family Expansion Predic- tion (MFEP) as predicting the increase in the size of a morphological family. We create a ten-year Reddit corpus as a benchmark for MFEP and evaluate a number of baselines on this benchmark. Our experiments demonstrate very good performance on MFEP. 1 Introduction Lexical change is a prime indicator of topical dy- namics in social media. When people or events attract the attention of a user community, this is reflected by the token frequency evolution of in- dividual words. The burst in token frequency of the word “trump” in social media before the 2016 presidential election (see Figure 1), e.g., mirrors the increasing presence of Donald Trump in public discourse during that time. However, token frequency is only one way of measuring changes in topical prominence. Accom- panying the increase in token frequency of “trump”, there was a parallel increase in the number of words morphologically related to “trump”, i.e., words like “trumpification”, “antitrumpism”, and “detrumpify” (see Figure 1, Table 1). Most of these words have a very low token frequency and are removed in the first steps of a typical NLP pipeline. Here, we present the first study of lexical change in social media that takes as its main unit of analy- sis the type frequency evolution of morphological families, i.e., changes in the number of morpholog- ically related words such as “trump”, “trumpifica- tion”, “antitrumpism”, and “detrumpify”. We show that morphological families allow for a fresh view Figure 1: Token frequency of “trump” and type fre- quency of derivations of “trump” in the r/politics Subreddit between 08/2007 and 07/2018. Vertical line: date of the 2016 presidential election. Month Words 04/2015 “trump” 05/2015 “trump”, “trumpish”, “trumpster”, “trumpy” 06/2015 “trump”, “trumpening”, “trumper”, “trumpish”, “trumpness”, “trumpology”, “trumpster”, “trumpy” 07/2015 “trump”, “trumpening”, “trumper”, “trumpic”, “trumpification”, “trumpiness”, “trumpish”, “trumpism”, “trumpistan”, “trumpness”, “trumpster”, “trumpy” Table 1: Derivations of “trump” in four subsequent months of the r/politics Subreddit. of lexical change in social media, making them a promising tool for studies in the social sciences that draw on NLP techniques. At the same time, our work adds to the growing body of computa- tional research on derivational morphology (Cot- terell et al., 2017; Vylomova et al., 2017; Cotterell and Sch¨utze, 2018; Deutsch et al., 2018; Pierre- humbert and Granell, 2018; Hofmann et al., 2020) by introducing a temporal perspective. Contributions. We introduce the novel task of Morphological Family Expansion Prediction (MFEP), which …

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