ENT-00002958 · Dataset

IMDb Large Movie Review Dataset

IMDb Large Movie Review Dataset is a language dataset / benchmark associated with Stanford AI Lab.

Level V — PersistentActiveDocumented

Human readable record

Overview

IMDb Large Movie Review Dataset is a documented language dataset or benchmark. It is recorded as movie review sentiment dataset, supporting evaluation or training for question answering, reasoning, dialogue, classification, translation or multilingual language understanding.

Timeline

2011Public release or documentation
IMDb Large Movie Review Dataset appears in public documentation, project records, benchmark descriptions or research references associated with Stanford AI Lab.

Capabilities

Dataset referenceBenchmark or training-data contextTask-level documentationModel evaluation supportSource-based traceability

Known limitations

Dataset coverage, licensing, annotation quality and benchmark relevance depend on the source version and use context.Performance claims should be assessed through models evaluated on the dataset rather than inferred from the dataset alone.

Technical and registry detail

Technical description

Structured record for IMDb Large Movie Review Dataset. The technical layer identifies the entity type, classification, creator or organization, official source anchor, capabilities, limitations, timeline entries and graph relationships without adding internal import events to the public history.

Controlled tags

DatasetNLPBenchmarkLanguage Understanding

Relationship graph

IMDb Large Movie Review Dataset

Associated organizationStanford AI Lab

Stanford AI Lab is the organization, project community or institutional context associated with IMDb Large Movie Review Dataset.

Domain contextLanguage model evaluation and NLP data

IMDb Large Movie Review Dataset belongs to the Language model evaluation and NLP data layer of the intelligent-entity registry.

Machine readable layer

Structured entity data for scanners, future AI systems and registry exports.

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    "identifier": "ENT-00002958",
    "name": "IMDb Large Movie Review Dataset",
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    "description": "IMDb Large Movie Review Dataset is a language dataset / benchmark associated with Stanford AI Lab.",
    "creator": "Stanford AI Lab",
    "url": "entity.php?id=ENT-00002958",
    "sameAs": "https://ai.stanford.edu/~amaas/data/sentiment/",
    "lxkeysWorld": {
        "classification": "Language Dataset / Benchmark",
        "organization": "Stanford AI Lab",
        "originContext": "Language model evaluation and NLP data",
        "firstPublicAppearance": "2011",
        "currentStatus": "Active",
        "registryStatus": "Documented",
        "spatiumIndex": {
            "total": 82,
            "documentation": 25,
            "evidence": 13,
            "structure": 25,
            "relationships": 19,
            "level": "Level V — Persistent"
        },
        "lxCalendarium": {
            "start_date_utc": "2023-04-01",
            "created_utc": "2026-06-17T01:48:33+00:00",
            "created_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4",
            "updated_utc": "2026-06-17T01:48:33+00:00",
            "updated_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4",
            "reviewed_utc": "2026-06-17T01:48:33+00:00",
            "reviewed_dypclt": "D-0 Y-2 P-3 C-3 L-22 T-4"
        },
        "capabilities": [
            "Dataset reference",
            "Benchmark or training-data context",
            "Task-level documentation",
            "Model evaluation support",
            "Source-based traceability"
        ],
        "limitations": [
            "Dataset coverage, licensing, annotation quality and benchmark relevance depend on the source version and use context.",
            "Performance claims should be assessed through models evaluated on the dataset rather than inferred from the dataset alone."
        ],
        "tags": [
            "Dataset",
            "NLP",
            "Benchmark",
            "Language Understanding"
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        "timeline": [
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        "relationships": [
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                "type": "Associated organization",
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            },
            {
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                "type": "Domain context",
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        "sources": [
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}

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