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{
    "pk": 49356,
    "title": "Fine-tuning semantic vectors with semantic fluency data",
    "subtitle": null,
    "abstract": "Semantic vectors derived from training on large text corpora (e.g., word2vec, BERT) are widely used as a methodological tool to model similarity of concepts. Recent work has demonstrated that a small amount of human training data can be used to fine-tune these vectors for modeling specific tasks. For example, human ratings of pairwise similarity can be used to estimate a set of dimensional weights, and these weights can improve estimates of human similarity ratings for held-out pairs. We applied this methodology to the semantic fluency task (listing items from a category) and find that category- specific weights can be used to identify the semantic category of a fluency list. The results have methodological implications for modeling retrieval in semantic fluency tasks, estimating semantic representations, and identifying semantic clusters and switches in fluency data.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Linguistics; Psychology; Memory; Representation; Semantics of language; Computational Modeling; Knowledge representation"
        }
    ],
    "section": "Papers with Poster Presentation",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/06p223vj",
    "frozenauthors": [
        {
            "first_name": "Jeffrey",
            "middle_name": "",
            "last_name": "Zemla",
            "name_suffix": "",
            "institution": "Syracuse University",
            "department": ""
        },
        {
            "first_name": "Nichol",
            "middle_name": "",
            "last_name": "Castro",
            "name_suffix": "",
            "institution": "University at Buffalo",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2025-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/49356/galley/37317/download/"
        }
    ]
}