Seeing through the mess: evolutionary dynamics of lexical polysemy

Seeing through the mess: evolutionary dynamics of lexical polysemy

Abstract

Evidently, words can have multiple senses. For example, the word mess refers to a place to have food or to a confusing situation. How exactly multiple senses emerge is less clear. In this work, we propose and analyze a mathematical model of the evolution of lexical meaning to investigate mechanisms leading to polysemy. This model features factors that have been discussed to impact the semantic processing and transmission of words: word frequency, non-conformism, and semantic discriminability. We formally derive conditions under which a sense of a word tends to diversify itself into multiple senses that coexist stably. The model predicts that diversification is promoted by low frequency, a strong bias for non-conformist usage, and high semantic discriminability. We statistically validate these predictions with historical language data covering semantic developments of a set of English words. Multiple alternative measures are used to operationalize each variable involved, and we confirm the predicted tendencies for twelve combinations of measures.

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Authors
  • Baumann, Andreas
  • Stephan, Andreas
  • Roth, Benjamin
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Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
The 2023 Conference on Empirical Methods in Natural Language Processing
Divisions
Data Mining and Machine Learning
Subjects
Kuenstliche Intelligenz
Event Location
Singapore
Event Type
Conference
Event Dates
6-10 Dec, 2023
Series Name
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Date
December 2023
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