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Semantic distance norms computed from an electronic dictionary (WordNet)

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TLDR
Semantic distance as derived from WordNet appears distinct from other measures of word pair relatedness and is psychologically functional.
Abstract
WordNet, an electronic dictionary (or lexical database), is a valuable resource for computational and cognitive scientists. Recent work on the computing of semantic distances among nodes (synsets) in WordNet has made it possible to build a large database of semantic distances for use in selecting word pairs for psychological research. The database now contains nearly 50,000 pairs of words that have values for semantic distance, associative strength, and similarity based on co-occurrence. Semantic distance was found to correlate weakly with these other measures but to correlate more strongly with another measure of semantic relatedness, featural similarity. Hierarchical clustering analysis suggested that the knowledge structure underlying semantic distance is similar in gross form to that underlying featural similarity. In experiments in which semantic similarity ratings were used, human participants were able to discriminate semantic distance. Thus, semantic distance as derived from WordNet appears distinct from other measures of word pair relatedness and is psychologically functional. This database may be downloaded from www.psychonomic.org/archive/.

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

TL;DR: The database will be useful for investigators interested in cuing, priming, recognition, network theory, linguistics, and implicit testing applications, and for evaluating the predictive value of free association probabilities as compared with other measures, such as similarity ratings and co-occurrence norms.
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Representing word meaning and order information in a composite holographic lexicon.

TL;DR: The authors used simple convolution and superposition mechanisms to learn distributed holographic representations for words, which can be used for higher order models of language comprehension, relieving the complexity required at the higher level.

Representing Word Meaning and Order Information in a Composite

TL;DR: A computational model that builds a holographic lexicon representing both word meaning and word order from unsupervised experience with natural language demonstrates that a broad range of psychological data can be accounted for directly from the structure of lexical representations learned in this way, without the need for complexity to be built into either the processing mechanisms or the representations.
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Activating event knowledge

TL;DR: It is concluded that event-based relations are encoded in semantic memory and computed as part of word meaning, and have a strong influence on language comprehension.
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Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation

TL;DR: It is argued that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated and that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources.
References
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Journal ArticleDOI

WordNet : an electronic lexical database

Christiane Fellbaum
- 01 Sep 2000 - 
TL;DR: The lexical database: nouns in WordNet, Katherine J. Miller a semantic network of English verbs, and applications of WordNet: building semantic concordances are presented.
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A spreading-activation theory of semantic processing

TL;DR: The present paper shows how the extended theory can account for results of several production experiments by Loftus, Juola and Atkinson's multiple-category experiment, Conrad's sentence-verification experiments, and several categorization experiments on the effect of semantic relatedness and typicality by Holyoak and Glass, Rips, Shoben, and Smith, and Rosch.
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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.
Journal ArticleDOI

Word association norms, mutual information, and lexicography

TL;DR: The proposed measure, the association ratio, estimates word association norms directly from computer readable corpora, making it possible to estimate norms for tens of thousands of words.
Posted Content

Using Information Content to Evaluate Semantic Similarity in a Taxonomy

TL;DR: In this article, a new measure of semantic similarity in an IS-A taxonomy based on the notion of information content is presented, and experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r < 0.90 for human subjects performing the same task).
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