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dc.contributor.authorJackson, Alice F.
dc.contributor.authorBolger, Donald J.
dc.date.accessioned2014-10-14T16:38:55Z
dc.date.available2014-10-14T16:38:55Z
dc.date.issued2014-05-12
dc.identifierhttps://doi.org/10.13016/M24G7C
dc.identifier.citationJackson AF and Bolger DJ (2014) Using a high-dimensional graph of semantic space to model relationships among words. Front. Psychol. 5:385. doi: 10.3389/fpsyg.2014.00385en_US
dc.identifier.urihttp://hdl.handle.net/1903/15848
dc.descriptionFunding for Open Access provided by the UMD Libraries Open Access Publishing Fund.
dc.description.abstractThe GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD).en_US
dc.language.isoen_USen_US
dc.publisherFrontiers in Psychologyen_US
dc.subjectgraphen_US
dc.subjectcomputational model of languageen_US
dc.subjectsimilarityen_US
dc.subjectco-occurrenceen_US
dc.subjectdistribution modelen_US
dc.titleUsing a high-dimensional graph of semantic space to model relationships among wordsen_US
dc.typeArticleen_US
dc.relation.isAvailableAtCollege of Educationen_us
dc.relation.isAvailableAtHuman Development & Quantitative Methodologyen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us


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