Data Structure ?
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Data Structure ?
*The above analysis is the result extracted and analyzed by the system, and the specific actual data shall prevail.
README.md
人間のアノテーターによるラベルアノテーションの誤りは、固有表現抽出（NER）に2つの課題をもたらします。
テストセットの誤りは、評価結果に干渉し、さらにはモデル性能の不正確な評価につながる可能性があります。
トレーニングセットの誤りは、NERモデルのトレーニングに悪影響を及ぼす可能性があります。
これら2つの問題に対処するために：
テストセットの誤りを手動で修正し、よりクリーンなベンチマークを作成します。
トレーニングセットの誤りを処理するためのフレームワークCrossWeighを開発します。
内容
CrossWeighは、重み付きのトレーニングインスタンスを受け付ける任意のNERアルゴリズムと連携します。これは2つのモジュールで構成されています。1) 誤り推定：相互チェックプロセスを通じて、トレーニングデータ内の潜在的な誤りが特定される部分と、2) 誤り重み付け：最終的なNERモデルのトレーニング中に、それらの誤りの重みが下げられる部分です。
謝辞
Named-Entity-Recognition-NER-Papers
劉鹏飞、傅錦蘭および他の貢献者。
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- 1. The data originates from internet data collection or provided by service providers, and this platform offers users the ability to view and browse datasets.
- 2. This platform serves only as a basic information display for datasets, including but not limited to image, text, video, and audio file types.
- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
- 1. All datasets on this site are copyrighted by their original publishers or data providers.
- 1. If you need to repost data from this site, please retain the original data source URL and related copyright notices.
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