Deep neural network models have helped named entity (NE) recognition achieve amazing performance without handcrafting features. However, existing systems require large amounts of human annotated training data. Efforts have been made to replace human annotations with external knowledge (e.g., NE dictionary, part-of-speech tags), while it is another challenge to obtain such effective resources. In this work, we propose a fully unsupervised NE recognition model which only needs to take informative clues from pre-trained word embeddings. We first apply Gaussian Hidden Markov Model and Deep Autoencoding Gaussian Mixture Model on word embeddings for entity span detection and type prediction, and then further design an instance selector based on reinforcement learning to distinguish positive sentences from noisy sentences and refine these coarse-grained annotations through neural networks. Extensive experiments on CoNLL benchmark datasets demonstrate that our proposed light NE recognition model achieves remarkable performance without using any annotated lexicon or corpus.

本文提出了一种基于预训练词嵌入的全无监督命名实体识别模型，使用高斯隐马尔可夫模型和深度自编码高斯混合模型进行实体跨度检测和类型预测，并基于强化学习设计实例选择器，通过神经网络消除杂乱注释，实现了无需使用任何标注词典或语料库，表现出色。

仅基于词嵌入的命名实体识别