  Mention detection is an important preprocessing step for annotation and
interpretation in applications such as NER and coreference resolution, but few
stand-alone neural models have been proposed able to handle the full range of
mentions. In this work, we propose and compare three neural network-based
approaches to mention detection. The first approach is based on the mention
detection part of a state of the art coreference resolution system; the second
uses ELMO embeddings together with a bidirectional LSTM and a biaffine
classifier; the third approach uses the recently introduced BERT model. Our
best model (using a biaffine classifier) achieves gains of up to 1.8 percentage
points on mention recall when compared with a strong baseline in a HIGH RECALL
coreference annotation setting. The same model achieves improvements of up to
5.3 and 6.2 p.p. when compared with the best-reported mention detection F1 on
the CONLL and CRAC coreference data sets respectively in a HIGH F1 annotation
setting. We then evaluate our models for coreference resolution by using
mentions predicted by our best model in start-of-the-art coreference systems.
The enhanced model achieved absolute improvements of up to 1.7 and 0.7 p.p.
when compared with our strong baseline systems (pipeline system and end-to-end
system) respectively. For nested NER, the evaluation of our model on the GENIA
corpora shows that our model matches or outperforms state-of-the-art models
despite not being specifically designed for this task.
