  Named entity recognition (NER) identifies typed entity mentions in raw text.
While the task is well-established, there is no universally used tagset: often,
datasets are annotated for use in downstream applications and accordingly only
cover a small set of entity types relevant to a particular task. For instance,
in the biomedical domain, one corpus might annotate genes, another chemicals,
and another diseases---despite the texts in each corpus containing references
to all three types of entities. In this paper, we propose a deep structured
model to integrate these "partially annotated" datasets to jointly identify all
entity types appearing in the training corpora. By leveraging multiple
datasets, the model can learn robust input representations; by building a joint
structured model, it avoids potential conflicts caused by combining several
models' predictions at test time. Experiments show that the proposed model
significantly outperforms strong multi-task learning baselines when training on
multiple, partially annotated datasets and testing on datasets that contain
tags from more than one of the training corpora.
