手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 構造化テキスト抽出× | 固有表現抽出（NER）× | |
|---|---|---|
| 分野 | テキストマイニング | テキストマイニング |
| 系統 | Process / pipeline | Process / pipeline |
| 提唱年 | — | — |
| 提唱者 | — | — |
| 種類≠ | Document-processing pipeline | NLP sequence-labelling task |
| 原典≠ | Zhu, J. et al. (2021). TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content. ACL. link ↗ | Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes. link ↗ |
| 別名≠ | form extraction, table extraction, document parsing, Yapılandırılmış Veri Çıkarma (Form & Tablo Çıkarma) | NER, entity tagging, Adlandırılmış Varlık Tanıma (NER) |
| 関連≠ | 2 | 3 |
| 概要≠ | Structured text extraction is a document-processing pipeline that automatically identifies and pulls tables, form fields, and structured data from PDF, HTML, and scanned documents. It converts heterogeneous document layouts into machine-readable, analysis-ready records and is widely used in data collection workflows, document digitisation projects, and academic corpus construction. | Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use. |
| ScholarGateデータセット ↗ |
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