The origins of Zipf's meaning-frequency law

Abstract

In his pioneering research, G. K. Zipf observed that more frequent words tend to have more meanings, and showed that the number of meanings of a word grows as the square root of its frequency. He derived this relationship from two assumptions: that words follow Zipf's law for word frequencies (a power law dependency between frequency and rank) and Zipf's law of meaning distribution (a power law dependency between number of meanings and rank). Here we show that a single assumption on the joint probability of a word and a meaning suffices to infer Zipf's meaning-frequency law or relaxed versions. Interestingly, this assumption can be justified as the outcome of a biased random walk in the process of mental exploration.

References in corpus (7)

• Entropy Rate of Diffusion Processes on Complex Networks
• Maximal-entropy random walks in complex networks with limited information
• Large-scale analysis of Zipf's law in English texts
• Compression and the origins of Zipf's law for word frequencies
• Voter models on weighted networks
• Log-log Convexity of Type-Token Growth in Zipf's Systems
• Random Walks on Complex Networks

Cited by in corpus (6)

• Linguistic laws in biology
• Polysemy and brevity versus frequency in language
• Domain-based Latent Personal Analysis and its use for impersonation detection in social media
• Parallels of human language in the behavior of bottlenose dolphins
• Zipf's laws of meaning in Catalan
• The advent and fall of a vocabulary learning bias from communicative efficiency