With the fast development of Deep Learning techniques, Named Entity Recognition (NER) is becoming more and more important in the information extraction task. The greatest difficulty that the NER task faces is to keep the detectability even when types of NE and documents are unfamiliar. Realizing that the specificity information may contain potential meanings of a word and generate semantic-related features for word embedding, we develop a distribution-aware word embedding and implement three different methods to make use of the distribution information in a NER framework. And the result shows that the performance of NER will be improved if the word specificity is incorporated into existing NER methods.

翻译：随着深度学习技术的快速发展，命名实体识别（NER）在信息抽取任务中变得愈发重要。NER任务面临的最大挑战是即使实体类型和文档类型均不熟悉时，仍需保持可检测性。鉴于特异性信息可能包含词语的潜在含义，并为词嵌入生成语义相关特征，我们开发了一种分布感知词嵌入方法，并通过三种不同方式将分布信息融入NER框架。实验结果表明，将词语特异性纳入现有NER方法后，其性能将得到有效提升。