Article Instance

HTTP 200 OK
Allow: GET
Content-Type: application/json
Vary: Accept

{
    "pk": 50398,
    "title": "Measuring the Semantic Consistency of Ordinal Annotations via Text Embedding Spaces and Its Applications",
    "subtitle": null,
    "abstract": "We propose a method for measuring the consistency of ordinal annotations based on a pre-trained embedding vector space. Intuitively, our method finds a direction in the embedding space along which data points align as closely as possible to their annotated ranks. The proposed approach guarantees a globally optimal solution that is free from approximation errors. Thus, it yields a unique consistency measure given a dataset with human-provided ordinal annotations and a pre-trained embedding model. This feature facilitates a wide range of applications, including not only ordinal prediction but also the unsupervised detection of annotation errors within datasets, as well as consistency assessment of stage-based scales (e.g., whether the transitions \"beginner to intermediate\" and \"intermediate to advanced\" form linear progressions in the embedding space) during dataset construction. We evaluate our method using real-world datasets with ordinal annotations to demonstrate its effectiveness.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "Education; Cognitive development; Language understanding; Machine learning; Computer-based experiment"
        }
    ],
    "section": "Member Abstracts with Poster Presentation",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/9hm9z41w",
    "frozenauthors": [
        {
            "first_name": "Yo",
            "middle_name": "",
            "last_name": "Ehara",
            "name_suffix": "",
            "institution": "Tokyo Gakugei University",
            "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/50398/galley/38360/download/"
        }
    ]
}