In this paper, we explore a new approach to named entity recognition (NER)
with the goal of learning from context and fragment features more effectively,
contributing to the improvement of overall recognition performance. We use the
recent fixed-size ordinally forgetting encoding (FOFE) method to fully encode
each sentence fragment and its left-right contexts into a fixed-size
representation. Next, we organize the context and fragment features into
groups, and feed each feature group to dedicated fully-connected layers.
Finally, we merge each group's final dedicated layers and add a shared layer
leading to a single output. The outcome of our experiments show that, given
only tokenized text and trained word embeddings, our system outperforms our
baseline models, and is competitive to the state-of-the-arts of various
well-known NER tasks.

通过使用 FOFE 方法，将多个特征组织成组并将其馈送到专用全连接层，本研究可有效地从上下文和片段特征中学习，并提高命名实体识别 (NER) 的整体性能。实验证明，在仅给定分词文本和训练过的单词嵌入时，本文提出的方法优于基准模型，并与各种著名的 NER 任务的最新技术水平相匹配。