  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.
