Methoden vergleichen

	Strukturierte Textextraktion	Information Extraction	Benannte Entitätenerkennung (NER)
Fachgebiet	Text Mining	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	—	—	—
Urheber	—	—	—
Typ	Document-processing pipeline	NLP structured-information task	NLP sequence-labelling task
Wegweisende Quelle	Zhu, J. et al. (2021). TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content. ACL.	Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Aliasnamen	form extraction, table extraction, document parsing, Yapılandırılmış Veri Çıkarma (Form & Tablo Çıkarma)	IE, structured information extraction, Bilgi Çıkarma (Information Extraction)	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Verwandt	2	4	3
Zusammenfassung	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.	Information extraction (IE) is a natural-language-processing task that converts unstructured text into structured information — such as events, relations, and attributes — so that facts buried in free-form documents become machine-readable records. The task was consolidated in early surveys by Cowie and Lehnert (1996) and later by Grishman (2012).	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.