Semi-Supervised Neural Networks for Nested Named Entity Recognition
| Type of publication: | Inproceedings |
| Citation: | nam2014semisupner |
| Booktitle: | Workshop on GermEval 2014 Named Entity Recognition Shared Task, KONVENS |
| Year: | 2014 |
| Pages: | 144-148 |
| Location: | Hildesheim |
| URL: | http://opus.bsz-bw.de/ubhi/volltexte/2014/308/pdf/03_09.pdf |
| Abstract: | In this paper, we investigate a semi-supervised learning approach based on neural networks for nested named entity recognition 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 architecture to deal with named entities in both levels simultaneously and to improve generalization performance on the classes that have a small number of labelled examples. |
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