Dependency tree structures capture long-distance and syntactic relationships
between words in a sentence. The syntactic relations (e.g., nominal subject,
object) can potentially infer the existence of certain named entities. In
addition, the performance of a named entity recognizer could benefit from the
long-distance dependencies between the words in dependency trees. In this work,
we propose a simple yet effective dependency-guided LSTM-CRF model to encode
the complete dependency trees and capture the above properties for the task of
named entity recognition (NER). The data statistics show strong correlations
between the entity types and dependency relations. We conduct extensive
experiments on several standard datasets and demonstrate the effectiveness of
the proposed model in improving NER and achieving state-of-the-art performance.
Our analysis reveals that the significant improvements mainly result from the
dependency relations and long-distance interactions provided by dependency
trees.

提出了一种简单而有效的依赖树导向的 LSTM-CRF 模型，以对命名实体识别（NER）任务中的完整依赖树进行编码并捕捉其相关属性，从而显著提高 NER 和实现领先水平，并发现依赖关系和依赖树提供的长距离交互是其主要原因。

基于依存的 LSTM-CRF 命名实体识别

Dependency-Guided LSTM-CRF for Named Entity Recognition

Named entity recognition, and other information extraction tasks, frequently
use linguistic features such as part of speech tags or chunkings. For languages
where word boundaries are not readily identified in text, word segmentation is
a key first step to generating features for an NER system. While using word
boundary tags as features are helpful, the signals that aid in identifying
these boundaries may provide richer information for an NER system. New
state-of-the-art word segmentation systems use neural models to learn
representations for predicting word boundaries. We show that these same
representations, jointly trained with an NER system, yield significant
improvements in NER for Chinese social media. In our experiments, jointly
training NER and word segmentation with an LSTM-CRF model yields nearly 5%
absolute improvement over previously published results.

本文研究利用神经网络模型共同训练中文社交媒体的命名实体识别（NER）和分词（word segmentation）任务，与之前的研究相比，使用 LSTM-CRF 模型得到了将近 5% 的显著提高。