Semi-Supervised Neural Networks for Nested Named Entity Recognition
Jinseok Nam · HilDok – Institutional Repository (Universität Hildesheim) · 2014
In this paper, we investigate a semisupervised 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.