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We propose Neural Entity Reasoner (NE-Reasoner), a framework to introduceglobal consistency of recognized entities into Neural Reasoner over NamedEntity Recognition (NER) task. Given an input sentence, the NE-Reasoner layercan infer over multiple entities to increase the global consistency of outputlabels, which then be transfered into entities for the input of next layer.NE-Reasoner inherits and develops some features from Neural Reasoner 1) asymbolic memory, allowing it to exchange entities between layers. 2) thespecific interaction-pooling mechanism, allowing it to connect each local wordto multiple global entities, and 3) the deep architecture, allowing it tobootstrap the recognized entity set from coarse to fine. Like human beings,NE-Reasoner is able to accommodate ambiguous words and Name Entities thatrarely or never met before. Despite the symbolic information the modelintroduced, NE-Reasoner can still be trained effectively in an end-to-endmanner via parameter sharing strategy. NE-Reasoner can outperform conventionalNER models in most cases on both English and Chinese NER datasets. For example,it achieves state-of-art on CoNLL-2003 English NER dataset.

Neural Entity Reasoner for Global Consistency in NER