Article Instance

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{
    "pk": 33030,
    "title": "Using High-dimensional Semantic Spaces Derived from Large Text Corpora",
    "subtitle": null,
    "abstract": "Attempting to derive models of semantic memory using \npsychometric techniques has a long history in cognitive \npsychology dating back at least to Osgood (1957). Many others \nhave used multidimensional scaling on human judgements of \nsimilarity (e.g., Shepard, 1962, 1974; Rips, Shoben, & Smith, \n1973; Schvaneveldt, 1990). Recently, a small group of \ninvestigators have been using large corpora, 1 million to 500 \nmillion words, to develop cognitively plausible \nhigh-dimensional semantic models without the need for human \njudgements on stimuli. These models have become increasingly \nbetter at explaining a wide range of cognitive pheno",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "17",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/3s1164pd",
    "frozenauthors": [
        {
            "first_name": "Curt",
            "middle_name": "",
            "last_name": "Burgess",
            "name_suffix": "",
            "institution": "University of California Riverside",
            "department": ""
        },
        {
            "first_name": "Gary",
            "middle_name": "",
            "last_name": "Cottrell",
            "name_suffix": "",
            "institution": "University of California, San Diego",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1995-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
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            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/33030/galley/24092/download/"
        }
    ]
}