  In this paper, we report our discovery on named entity distribution in a
general word embedding space, which helps an open definition on multilingual
named entity definition rather than previous closed and constraint definition
on named entities through a named entity dictionary, which is usually derived
from human labor and replies on schedule update. Our initial visualization of
monolingual word embeddings indicates named entities tend to gather together
despite of named entity types and language difference, which enable us to model
all named entities using a specific geometric structure inside embedding space,
namely, the named entity hypersphere. For monolingual cases, the proposed named
entity model gives an open description of diverse named entity types and
different languages. For cross-lingual cases, mapping the proposed named entity
model provides a novel way to build a named entity dataset for resource-poor
languages. At last, the proposed named entity model may be shown as a handy
clue to enhance state-of-the-art named entity recognition systems generally.
