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Combining Minimally-supervised Methods for Arabic Named Entity Recognition

Althobaiti, M., Kruschwitz, Udo and Poesio, Massimo (2015) Combining Minimally-supervised Methods for Arabic Named Entity Recognition. Transactions of the Association for Computational Linguistics TACL 3 (3), pp. 243-256.

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

Other URL: https://transacl.org/ojs/index.php/tacl/article/view/564


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. ...

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Item type:Article
Date:2015
Institutions:Languages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Lehrstuhl für Informationswissenschaft
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
Owner only: item control page

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