Named entity recognition (NER) models are typically based on the architecture
of Bi-directional LSTM (BiLSTM). The constraints of sequential nature and the
modeling of single input prevent the full utilization of global information
from larger scope, not only in the entire sentence, but also in the entire
document (dataset). In this paper, we address these two deficiencies and
propose a model augmented with hierarchical contextualized representation:
sentence-level representation and document-level representation. In
sentence-level, we take different contributions of words in a single sentence
into consideration to enhance the sentence representation learned from an
independent BiLSTM via label embedding attention mechanism. In document-level,
the key-value memory network is adopted to record the document-aware
information for each unique word which is sensitive to similarity of context
information. Our two-level hierarchical contextualized representations are
fused with each input token embedding and corresponding hidden state of BiLSTM,
respectively. The experimental results on three benchmark NER datasets
(CoNLL-2003 and Ontonotes 5.0 English datasets, CoNLL-2002 Spanish dataset)
show that we establish new state-of-the-art results.

本文提出了一种基于分层上下文表示的模型，通过句子级和文档级表示来提高实体识别模型中全局信息的利用，其中包括标签嵌入和注意机制以及键值内存网络，实验证明该模型在三个基准数据集上表现出优越的效果。