  Learning to detect entity mentions without using syntactic information can be
useful for integration and joint optimization with other tasks. However, it is
common to have partially annotated data for this problem. Here, we investigate
two approaches to deal with partial annotation of mentions: weighted loss and
soft-target classification. We also propose two neural mention detection
approaches: a sequence tagging, and an exhaustive search. We evaluate our
methods with coreference resolution as a downstream task, using multitask
learning. The results show that the recall and F1 score improve for all
methods.
