Salīdzināt metodes

	Saīsinājumu izvērsums	Nosaukuma entītiju atpazīšana (NER)
Nozare	Teksta ieguve	Teksta ieguve
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2003	—
Autors	Schwartz & Hearst (2003) — seminal algorithm for biomedical abbreviation detection	—
Tips	NLP disambiguation pipeline	NLP sequence-labelling task
Pirmavots	Schwartz, A.S. & Hearst, M.A. (2003). A Simple Algorithm for Identifying Abbreviation Definitions in Biomedical Text. Pacific Symposium on Biocomputing (PSB), 8, 451-462.	Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.
Citi nosaukumi	acronym resolution, abbreviation disambiguation, short-form expansion, Kısaltma ve Akronim Çözümleme	NER, entity tagging, Adlandırılmış Varlık Tanıma (NER)
Saistītās	4	3
Kopsavilkums	Abbreviation and acronym resolution is a natural-language-processing pipeline that maps each short form in a text to its full-length definition using contextual cues from the surrounding text. It is especially important in medical, legal, and technical documents, where the same acronym may carry entirely different meanings across domains. The field's foundational algorithm was published by Schwartz and Hearst (2003) for biomedical literature and has since been extended by neural and transformer-based approaches.	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.