NNE: A Dataset for Nested Named Entity Recognition in English Newswire
Table 2: NER results on NNE using different methods.
['[EMPTY]', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1']
The latter two models were proposed to recognize nested mentions. We use sections 02 as development set, sections 23 and 24 as test set, and the remaining sections as training set. The model that performs best on the development set is evaluated on the test set for the final result. We also combine the outputs from these two flat NER models, and denote the result as BiLSTM-CRF-BOTH. For example, the model pretrained on outermost (top) mentions has 38.0 recall, as around 60% of mentions are nested within others. However, its decoding speed of 9 words per second is slow due to the large number of entity categories of our dataset. The transition-based method has a higher decode speed of 57 words per second, but has much lower precision than flat NER models.