A Computational Evaluation of Two Laws of Semantic Change

By Yang Xu, Charles Kemp. 2015

86 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

Citations in the corpus

Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change (2016, methodology): “…with distributional methods (Sagi et al., 2011; Wijaya and Yeniterzi, 2011; Gulordava and Baroni, 2011; Jatowt and Duh, 2014; Kulkarni et al., 2014; Xu and Kemp, 2015), as well as recent work on detecting the emergence of novel word senses (Lau et al., 2012; Mitra et al., 2014; Cook et al., 2014;…”
Analysing Lexical Semantic Change with Contextualised Word Representations (2020, background): “…relevant for computational linguists to detect and characterise lexical semantic change—e.g., in the form of laws of semantic change (Dubossarsky et al., 2015; Xu and Kemp, 2015; Hamilton et al., 2016)—with the aid of quantitative and reproducible evaluation procedures (Schlechtweg et al., 2018).”
Harm inflation: Making sense of concept creep (2020, background): “One outcome of these technical advances has been the formulation and testing of laws of semantic change (e.g., Dubossarsky et al., 2017; Winter et al., 2014; Xu & Kemp, 2015), such as the relationship between rate of change and word frequency.”
Diachronic word embeddings and semantic shifts: a survey (2018, result): “…change: 1. word vectors can be expressed as linear combinations of their neighbors in previous time periods; 2. the meaning of words tend to decay linearly in time, in terms of the similarity of a word to itself; this is in line with the ‘ law of differentiation ’ proposed by Xu and Kemp (2015).”