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.

本研究尝试通过组合多个具有一定关系但不同内容的命名实体识别数据集，使用基于 FFNN 的局部探测多任务模型从变长单词序列生成理论上无损且唯一的固定大小表示，提高该领域任务的性能表现，实验结果表明，该模型在几乎所有任务中表现出有竞争力的性能。