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Semi-Supervised Neural Networks for Nested Named Entity Recognition

  • In this paper, we investigate a semi- supervised learning approach based on neu- ral networks for nested named entity recog- nition on the GermEval 2014 dataset. The dataset consists of triples of a word, a named entity associated with that word in the first-level and one in the second-level. Additionally, the tag distribution is highly skewed, that is, the number of occurrences of certain types of tags is too small. Hence, we present a unified neural network archi- tecture to deal with named entities in both levels simultaneously and to improve gen- eralization performance on the classes that have a small number of labelled examples.

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Author:Jinseok Nam
Parent Title (English):Workshop proceedings of the 12th edition of the KONVENS conference
Document Type:Conference Proceeding
Date of Publication (online):2014/11/25
Release Date:2014/11/25
Tag:NER; Named entity recognition
GND Keyword:Computerlinguistik
First Page:144
Last Page:148
PPN:Link zum Katalog
Institutes:Fachbereich III / Informationswissenschaft und Sprachtechnologie
DDC classes:400 Sprache / 400 Sprache, Linguistik
Collections:KONVENS 2014 / Workshop Proceedings of the 12th KONVENS 2014
Licence (German):License LogoCreative Commons - Namensnennung 3.0