Combining Minimally-supervised Methods for Arabic Named Entity Recognition

URN to cite this document:: urn:nbn:de:bvb:355-epub-403453
DOI to cite this document:: 10.5283/epub.40345

Althobaiti, M. ; Kruschwitz, Udo

; Poesio, Massimo

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Date of publication of this fulltext: 13 Jun 2019 13:22

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Item type:	Article
Journal or Publication Title:	Transactions of the Association for Computational Linguistics TACL
Publisher:	Association for Computational Linguistics
Volume:	3
Number of Issue or Book Chapter:	3
Page Range:	pp. 243-256
Date:	2015
Institutions:	Languages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Lehrstuhl für Informationswissenschaft (Prof. Dr. Udo Kruschwitz) Informatics and Data Science > Department Human-Centered Computing > Lehrstuhl für Informationswissenschaft (Prof. Dr. Udo Kruschwitz)
Dewey Decimal Classification:	000 Computer science, information & general works > 020 Library & information sciences
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Unknown
Item ID:	40345

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Abstract

Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. ...