The Big Book of Concepts

By Gregory L. Murphy. 2002

2,779 citations recorded by the source corpus.

Subfields: Education, Language and Linguistics

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Citations in the corpus

Recognition in Terra Incognita (2018, background): “These observations suggest that current algorithms rely mostly on rote patternmatching, rather than abstracting from the training set ‘visual concepts’ [15] that can generalize well to novel situations.”
THE DEVELOPMENT OF OBJECT CATEGORIZATION IN YOUNG CHILDREN: HIERARCHICAL INCLUSIVENESS, AGE, PERCEPTUAL ATTRIBUTE, AND GROUP VERSUS INDIVIDUAL ANALYSES (2010, background): “The transitivity of category membership leads to a similar transitivity of property inheritance (Murphy, 2002).”
An embarrassingly simple approach to zero-shot learning (2015, background): “Indeed, it is attributes learning that drives the possibility of learning unseen classes based only on their description [27].”
Multimodal Distributional Semantics (2013, background): “4 Humans are very good at grouping together words (or the concepts they denote) into classes based on their semantic relatedness (Murphy, 2002), therefore a cognitive-aware representation of meaning must show its proficiency also in categorization (e.g., Poesio & Almuhareb, 2005; Baroni et al.,…”
Distributional Memory: A General Framework for Corpus-Based Semantics (2010, neutral): “The common view in cognitive (neuro)science is that humans resort to a single semantic memory, a relatively stable long-term knowledge database, adapting the information stored there to the various tasks at hand (Murphy 2002; Rogers and McClelland 2004).”
A mathematical theory of semantic development in deep neural networks (2018, background): “Also, consistent with [3, 16, 17], we note that we cannot judge the coherence of a category without knowing about its relations to all other categories, as singular values are a complex emergent property of the entire environment.”
Distributional vectors encode referential attributes (2015, background): “A common noun such as dog denotes a concept , based on a prototype with fuzzy boundaries, susceptible of metaphorical extensions, and bearing all the other hallmarks of generic conceptual knowledge (Carlson, 2009; Murphy, 2002).”
Concepts and categories: Memory, meaning, and metaphysics (2012, methodology): “There has been such a consistent stream of research over this period that one critic of this literature, Gregory Murphy (2002), felt compelled to call his monograph, The Big Book of Concepts.”
The acquisition of tense-aspect: Converging evidence from corpora and telicity ratings (2009, background): “The average does not exist as an exemplar in real life for all categories; it does so much more for basic-level categories than for their more abstract superordinates (Murphy, 2003; Rosch et al.).”
Two Sides of the Same Coin (2012, background): “cognitive psychology indicates that experts can see both more differentiation and more commonality among categories and can switch between basic and subordinate levels of classification (Murphy, 2004).”
The misunderstood limits of folk science: an illusion of explanatory depth (2002, background): “The essentialist bias may liberally create placeholders that reserve mental locations for essences, in anticipation that the locations will be filled later (Murphy, 2002; Gelman & Koenig, 2002).”
Word meaning in minds and machines (2020, background): “That is, word learning can in ﬂ uence one ’ s concepts because word learning involves learning what kinds of things there are in the world (see Murphy, 2002, for more discussion).”
Describing objects by their attributes (2009, background): “Our notion of attributes comes from the literature on concepts and categories (reviewed in [15]).”
Legitimacy‐as‐Feeling: How Affect Leads to Vertical Legitimacy Spillovers in Transnational Governance (2014, neutral): “…associations between traits (Barsalou, 1985; 1991) and that they are constructed “online” by relying on preexisting knowledge structures (Lakoff, 1987; Murphy, 2002), we argue that the categorical prototype for TGSs and other network-based forms does not represent a summary representation of…”
Linguistic generalization and compositionality in modern artificial neural networks (2019, background): “For example, similarity-driven reasoning about concept instances is probably too fuzzy and prototype-based to be accounted for by systematic rules [24].”