The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth

Abstract

We present statistical analyses of the large-scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small-world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale-free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high-dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small-world statistics and power-law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks.

The company that words keep: comparing the statistical structure of child- versus adult-directed language

“…One way to measure associative relations is via free association norms, where one word is provided (the cue) and participants are asked to produce the first word that comes to mind (the target). Several studies have now shown that the number of cue words that produce a given target word in free association norms is correlated with children's age of acquisition of that target word (Hills, Maouene, Maouene, Sheya & Smith, 2009 ;Hills et al, 2010 ;Steyvers & Tenenbaum, 2005). In other words, if, in response to a broad range of cues, the word ball was produced more often as a target than zebra, then ball will tend to have an earlier age of acquisition.…”

“…Two adult-directed corpora were chosen to capture the diversity of adult-directed language. One was the Santa Barbara Corpus of Spoken American English (SBC), parts 1-4, which contained approximately 250,000 words, taken from sixty discourse recordings between adults, ranging from personal conversations to university lectures (Du Bois & Englebretson, 2000-2005. The SBC corpus is similar to the CHILDES corpus in that it represents largely conversational speech in American English.…”

“…One of the goals of the present study is to understand why adultgenerated free associates are correlated with the order of acquisition in early language learning (see Hills et al, 2009 ;Steyvers & Tenenbaum, 2005). The observation that child-directed language is more associative than adult-directed language offers two potential reasons.…”

Detecting order–disorder transitions in discourse: Implications for schizophrenia

“…One approach to achieve this is graph representation, with roots in semantic network theory (Collins and Loftus, 1975;Collins and Quillian, 1969;Steyvers and Tenenbaum, 2005). Graphs are mathematical objects consisting of sets of nodes and sets of edges connecting the nodes.…”

Exploration in free word association networks: models and experiment

“…These word association networks typically exhibit small-world structures, with short average distances between words, together with strong local clustering [24,11], a property shared with lexigraphical spaces obtained from word co-occurances [13].…”