Korean Named-Entity Recognition Method Based on Maximum Entropy Model and Neural Network Model

摘要

A Korean named entity recognition method based on a maximum entropy model and a neural network model, which includes: building a prefix tree dictionary, wherein when a template for any combined noun or a template of any proper noun is matched with an input sentence, the combined noun or proper noun is recognized as a target word; obtaining the target word from a target word selection module and searching for the target word in an entity dictionary, wherein when only one subcategory is matched, the subcategory is used as a tag for the target word; using the maximum entropy model and multiple kinds of linguistic information; constructing a feed-forward neural network mode; and combining adjacent words into an entity tag according to a template selection rule.