Abstract
Words shift in meaning for many reasons, including cultural factors like new technologies and regular linguistic processes like subjectification. Understanding the evolution of language and culture requires disentangling these underlying causes. Here we show how two different distributional measures can be used to detect two different types of semantic change. The first measure, which has been used in many previous works, analyzes global shifts in a word's distributional semantics; it is sensitive to changes due to regular processes of linguistic drift, such as the semantic generalization of promise ("I promise." "It promised to be exciting."). The second measure, which we develop here, focuses on local changes to a word's nearest semantic neighbors; it is more sensitive to cultural shifts, such as the change in the meaning of cell ("prison cell" "cell phone"). Comparing measurements made by these two methods allows researchers to determine whether changes are more cultural or linguistic in nature, a distinction that is essential for work in the digital humanities and historical linguistics.
Highlights
Distributional methods of embedding words in vector spaces according to their co-occurrence statistics are a promising new tool for diachronic semantics (Gulordava and Baroni, 2011; Jatowt and Duh, 2014; Kulkarni et al, 2014; Xu and Kemp, 2015; Hamilton et al, 2016)
We focused on verbs vs. nouns since they are the two major parts-of-speech and previous research has shown that verbs are more semantically mutable than nouns and more likely to undergo linguistic drift (Gentner and France, 1988), while nouns are far more likely to change due to cultural shifts like new technologies (Traugott and Dasher, 2001)
The global measure is more sensitive to changes in verbs, along with adjectives and adverbs, which are known to be the targets of many regular processes of linguistic change (Traugott and Dasher, 2001; Hopper and Traugott, 2003)
Summary
Distributional methods of embedding words in vector spaces according to their co-occurrence statistics are a promising new tool for diachronic semantics (Gulordava and Baroni, 2011; Jatowt and Duh, 2014; Kulkarni et al, 2014; Xu and Kemp, 2015; Hamilton et al, 2016). We show how two computational measures can be used to distinguish between semantic changes caused by cultural shifts (e.g., technological advancements) and those caused by more regular processes of semantic change (e.g., grammaticalization or subjectification). This distinction is essential for research on linguistic and cultural evolution. For advancing historical linguistics, cultural shifts amount to noise and only the more regular shifts matter
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