TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Nam, Jinseok
T1  - Semi-Supervised Neural Networks for Nested Named Entity Recognition
T2  - Workshop proceedings of the 12th edition of the KONVENS conference
N2  - 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.
KW  - Computerlinguistik
KW  - NER
KW  - Named entity recognition
Y1  - 2014
U6  - https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-3085
UN  - https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-3085
SP  - 144
EP  - 148
ER  -