Chinese Named Entity Recognition Augmented with Lexicon Memory

Inspired by the concept of content-addressable retrieval from cognitive science, we propose a novel fragment-based Chinese named entity recognition (NER) model augmented with a lexicon-based memory in which both character-level and word-level features are combined to generate better feature representations for possible entity names. Observing that the boundary information of entity names is particularly useful to locate and classify them into pre-defined categories, position-dependent features, such as prefix and suffix, are introduced and taken into account for NER tasks in the form of distributed representations. The lexicon-based memory is built to help generate such position-dependent features and deal with the problem of out-of-vocabulary words. Experimental results show that the proposed model, called LEMON, achieved state-of-the-art performance with an increase in the F1-score up to 3.2% over the state-of-the-art models on four different widely-used NER datasets.