- search hit 1 of 1
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.
| Author: | Jinseok Nam |
|---|---|
| URN: | https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-3085 |
| Parent Title (English): | Workshop proceedings of the 12th edition of the KONVENS conference |
| Document Type: | Conference Proceeding |
| Language: | English |
| 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): |  Creative Commons - Namensnennung 3.0 |
