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Open AccessJournal ArticleDOI

Semantic diversity: a measure of semantic ambiguity based on variability in the contextual usage of words.

TLDR
This work describes an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts, and suggests that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning.
Abstract
Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials.

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REBUS and the Anarchic Brain: Toward a Unified Model of the Brain Action of Psychedelics.

TL;DR: A leading model of global brain function, hierarchical predictive coding, is integrated with an often-cited model of the acute action of psychedelics, the entropic brain hypothesis, which states that psychedelics work to relax high-level priors, sensitising them to liberated bottom-up information flow, which can help guide and cultivate the revision of entrenched pathological priors.
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Emotion and language: valence and arousal affect word recognition.

TL;DR: Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects; this research demonstrates that incorporating emotional factors, especially valence, improves the performance of models of word recognition.
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Differing contributions of inferior prefrontal and anterior temporal cortex to concrete and abstract conceptual knowledge

TL;DR: Results converge with data from rTMS and neuropsychological investigations in demonstrating that representational content and task demands influence recruitment of different areas in the semantic network.
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In defense of abstract conceptual representations

TL;DR: The evidence supports a hierarchical model of knowledge representation in which modal systems provide a mechanism for concept acquisition and serve to ground individual concepts in external reality, whereas broadly conjunctive, supramodal representations play an equally important role in concept association and situation knowledge.
Journal ArticleDOI

The tool for the automatic analysis of lexical sophistication (TAALES): version 2.0

TL;DR: Newly added TAALes 2.0 indices, including those related to n-gram association strength, word neighborhood, and word recognition norms, featured heavily in these predictor models, suggesting that TAALES 2.1 represents a substantial upgrade.
References
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Journal ArticleDOI

WordNet: a lexical database for English

TL;DR: WordNet1 provides a more effective combination of traditional lexicographic information and modern computing, and is an online lexical database designed for use under program control.
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Finding Structure in Time

TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.
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A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.

TL;DR: A new general theory of acquired similarity and knowledge representation, latent semantic analysis (LSA), is presented and used to successfully simulate such learning and several other psycholinguistic phenomena.
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The MRC Psycholinguistic Database

TL;DR: A computerised database of psycholinguistic information is described, where semantic, syntactic, phonological and orthographic information about some or all of the 98,538 words in the database is accessible, by using a specially-written and very simple programming language.
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Contextual prerequisites for understanding: Some investigations of comprehension and recall

TL;DR: This article showed that relevant contextual knowledge is a prerequisite for comprehending prose passages and showed that providing Ss with the same information subsequent to the passages produced much lower comprehension ratings and recall scores.
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