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

By Mark Steyvers, Josh Tenenbaum. Cognitive Sciences, 2005

1,385 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a848b96512221f76c2fa15e3dd63a144f2875354

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Citations in the corpus

Computational Fact Checking from Knowledge Networks (2015, neutral): ““Barack Obama” “Married-To” “Michelle Obama”) [39].”
Graph-based term weighting for information retrieval (2012, methodology): “Such findings are used to improve navigation methods of intelligent systems (Sigman and Cecchi 2002; Steyvers and Tenenbaum 2005), and also in medicine, where it has been suggested that the effects of disconnecting the most connected vertices of cognitive association graphs can be identified in…”
Age-of-acquisition ratings for 30,000 English words (2012, background): “The robust additional effect of AoA was expected on the basis of theories of word learning (Izura et al., 2011;Monaghan & Ellis, 2010) and theories of the organization of the semantic system (Brysbaert et al., 2000; Sailor et al., 2011; Steyvers & Tenenbaum, 2005).”
Age of acquisition: its neural and computational mechanisms. (2007, background): “…locus hypothesis claims that early learned words have a semantic advantage over late learned words because they enter the representational network first and affect the semantic representations of later learned words (Brysbaert, Van Wijnendaele , & De Deyne, 2000; Steyvers & Tenenbaum, 2005).”
Languages cool as they expand: Allometric scaling and the decreasing need for new words (2012, background): “Hence, there are large spillovers in the size of the written corpus that follow from the intricate dependency structure of language stemming from the various grammatical roles [39, 40].”
How semantic memory structure and intelligence contribute to creative thought: a network science approach (2017, background): “…network science has identified mechanisms of language development through preferential attachment (Hills, Maouene, Maouene, Sheya, & Smith, 2009; Steyvers & Tenenbaum, 2005), has shown how specific semantic memory network parameters influence memory retrieval (Vitevitch, Chan, & Goldstein,…”
Representing word meaning and order information in a composite holographic lexicon. (2007, background): “More recent semantic networks learn new concepts and connections unsupervised and have large-scale learning structure that parallels human learning in many ways (Steyvers & Tenenbaum, 2005).”
Bots increase exposure to negative and inflammatory content in online social systems (2018, neutral): “We quantify the importance of concepts by identifying the hashtags with the highest degree, strength, and closeness centrality—characterizing number of different associations, total frequency of co-occurrences, and how closely hashtags are associated, respectively (26, 29, 30).”
Investigating the structure of semantic networks in low and high creative persons (2014, background): “In the linguistic domain, lexicons of different languages seem to display SWN characteristics, considered to be a fundamental principle in lexical organization (Steyvers and Tenenbaum, 2005; De-Deyne and Storms, 2008a,b; Borge-Holthoefer and Arenas, 2010; Kenett et al., 2011).”
The effect of age of acquisition: Partly frequency related, partly frequency independent (2006, background): “For example, Steyvers and Tenenbaum (2005) presented a mathematical model of a growing semantic network.”
Synchronization in complex networks (2008, methodology): “In [32] it is pointed out that, in general, the eigenratio R increases with increasing clustering in a modified version of the BA model [118].”
Age-of-acquisition effects in word and picture identification. (2005, background): “Steyvers and Tenenbaum (2005) recently incorporated the AoA effect into a simulation of a growing semantic network.”
Usage‐Based Language: Investigating the Latent Structures That Underpin Acquisition (2013, background): “…to be refined for clear communication, they are split, then split again hierarchically (e.g., animal, canine, dog, retriever, labrador), determines basic categorization, the structure of semantic classes, and the language form-semantic structure interface (Manin, 2008; Steyvers & Tennenbaum, 2005).”