Named entity recognition is a challenging task that has traditionally
required large amounts of knowledge in the form of feature engineering and
lexicons to achieve high performance. In this paper, we present a novel neural
network architecture that automatically detects word- and character-level
features using a hybrid bidirectional LSTM and CNN architecture, eliminating
the need for most feature engineering. We also propose a novel method of
encoding partial lexicon matches in neural networks and compare it to existing
approaches. Extensive evaluation shows that, given only tokenized text and
publicly available word embeddings, our system is competitive on the CoNLL-2003
dataset and surpasses the previously reported state of the art performance on
the OntoNotes 5.0 dataset by 2.13 F1 points. By using two lexicons constructed
from publicly-available sources, we establish new state of the art performance
with an F1 score of 91.62 on CoNLL-2003 and 86.28 on OntoNotes, surpassing
systems that employ heavy feature engineering, proprietary lexicons, and rich
entity linking information.

本文提出了一种新颖的神经网络模型，利用混合双向 LSTM 和 CNN 架构自动检测单词和字符级特征，并提出了一种将部分词典匹配编码到神经网络中的方法。经过广泛评估，使用两个来自公开资源的词典，该系统在 CoNLL-2003 数据集上达到 91.62 的 F1 分数，在 OntoNotes 上达到 86.28 的 F1 分数，超过了其他采用重度特征工程、专有词典和丰富的实体链接信息的系统。