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{
    "pk": 33090,
    "title": "Strong Semantic Systematicity from Unsupervised Connectionist Learning",
    "subtitle": null,
    "abstract": "A network exhibits strong semantic systematicity when, as \na result of training, it can assign appropriate meaning rep?resentations to novel sentences (both simple and embedded) \nwhich contain words in syntactic positions they did not oc?cupy during training. Herein we describe a network which \ndisplays strong semcintic systematicity in response to unsu?pervised tiaimng. During tradning, two-thirds of all nouns are \npresented only in a single syntactic position (either as gram?matical subject or object). Yet, during testing, the network \ncorrectly interprets thousands of sentences containing those \nnouns in novel positions. In addition, the network generalizes \nto novel levek of embedding. Successful training requires a \ncorpus of about 1000 sentences, and network training is quite \nrapid.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [],
    "section": "17",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/1n86r9t2",
    "frozenauthors": [
        {
            "first_name": "Robert",
            "middle_name": "F.",
            "last_name": "Hadley",
            "name_suffix": "",
            "institution": "Simon Eraser University",
            "department": ""
        },
        {
            "first_name": "Michael",
            "middle_name": "B.",
            "last_name": "Hayward",
            "name_suffix": "",
            "institution": "MPR Teltech Ltd.",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "1995-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/33090/galley/24151/download/"
        }
    ]
}