The University of South Florida free association, rhyme, and word fragment norms

By D. Nelson, C. McEvoy, Thomas A. Schreiber. Behavoir research methods, instruments & computers, 2004

2,306 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/78466285abd953288db4a9832e4616e1249dba19

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Citations in the corpus

Getting Explicit About the Implicit (2012, background): “…that target words share similar lexical frequency and length with their corresponding neutral words (this is true for both wordcompletion measures and the lexical decision task; Balota et al., 2007; Johnson & Saboe, 2011; Koopman et al., in press; Nelson et al., 2004; Tiggemanna et al., 2004).”
Line graphs, link partitions, and overlapping communities. (2009, methodology): “7: (Color online) The simple graph created from the South Florida Free Association Norms data [28], in the manner of [14].”
The effect of distinctive visual information on false recognition (2003, neutral): “Stimulus items were selected from the Nelson, McEvoy, and Schreiber (1998) database.”
Visualizing Similarity Data with a Mixture of Maps (2007, methodology): “Participants were presented with a list of English words as cues, and asked to respond to each word with a word which was “meaningfully related or strongly associated” [6].”
The New C Standard (Excerpted material) An Economic and Cultural Commentary (2008, neutral): “A long-term study (it started in 1973) by Nelson, McEvoy, and Schreiber[246] created the University of South Florida word association, rhyme, and word fragment norm database by collating the nearly threequarters of a million responses to 5,019 stimulus words from more than 6,000 subjects.”
Community detection in graphs (2009, methodology): “The network builds on the University of South Florida Free Association Norms (Nelson et al., 1998).”
Representing word meaning and order information in a composite holographic lexicon. (2007, background): “However, Steyvers (2000) has noted that many of the so-called semantic-only words in Chiarello et al,s (1990) experiment were, in fact, often associates in the D.”
Beyond mean response latency: Response time distributional analyses of semantic priming (2008, background): “Related targets were the primary associates of the primes according to the Nelson, McEvoy, and Schreiber (1998) norms, and unrelated prime-target pairs were not associates (i.e., forward and backward associate strength = .000).”
Integrating experiential and distributional data to learn semantic representations. (2009, background): “uk; see Kiss, Armstrong, Milroy, & Piper, 1973)—and one using speakers of American English—the Nelson association norms (Nelson et al., 2004).”
Embodiment of cognition and emotion (2015, background): “First, the critical property pairs (e.g., loud–rustle) were not associated in the Nelson, McEvoy, and Schreiber (1999) norms.”
Infinite multiple membership relational modeling for complex networks (2011, neutral): “9 Word relations in free association [19] Reuters911 13,314 148,038 -0.”
An Algorithm to Find Overlapping Community Structure in Networks (2007, background): ““Words” is a non-social network: a contiguous 1000-vertex subgraph of a word association network from [15], converted from an orig inal directed, weighted version [ 10 ].”
Evolution of Semantic Similarity—A Survey (2020, methodology): “• SimLex-999 [31]: 999 word pairs were selected from the UFS Dataset[67] of which 900 were similar and 99 were related but not similar.”