  Named entity recognition (NER) systems that perform well require task-related
and manually annotated datasets. However, they are expensive to develop, and
are thus limited in size. As there already exists a large number of NER
datasets that share a certain degree of relationship but differ in content, it
is important to explore the question of whether such datasets can be combined
as a simple method for improving NER performance. To investigate this, we
developed a novel locally detecting multitask model using FFNNs. The model
relies on encoding variable-length sequences of words into theoretically
lossless and unique fixed-size representations. We applied this method to
several well-known NER tasks and compared the results of our model to baseline
models as well as other published results. As a result, we observed competitive
performance in nearly all of the tasks.
