  Inspired by a concept of content-addressable retrieval from cognitive
science, we propose a novel fragment-based model augmented with a lexicon-based
memory for Chinese NER, in which both the character-level and word-level
features are combined to generate better feature representations for possible
name candidates. It is observed that locating the boundary information of
entity names is useful in order to classify them into pre-defined categories.
Position-dependent features, including prefix and suffix are introduced for NER
in the form of distributed representation. The lexicon-based memory is used to
help generate such position-dependent features and deal with the problem of
out-of-vocabulary words. Experimental results showed that the proposed model,
called LEMON, achieved state-of-the-art on four datasets.
