  Integrating lexicon into character-level sequence has been proven effective
to leverage word boundary and semantic information in Chinese named entity
recognition (NER). However, prior approaches usually utilize feature weighting
and position coupling to integrate word information, but ignore the semantic
and contextual correspondence between the fine-grained semantic units in the
character-word space. To solve this issue, we propose a Unified Lattice Graph
Fusion (ULGF) approach for Chinese NER. ULGF can explicitly capture various
semantic and boundary relations across different semantic units with the
adjacency matrix by converting the lattice structure into a unified graph. We
stack multiple graph-based intra-source self-attention and inter-source
cross-gating fusion layers that iteratively carry out semantic interactions to
learn node representations. To alleviate the over-reliance on word information,
we further propose to leverage lexicon entity classification as an auxiliary
task. Experiments on four Chinese NER benchmark datasets demonstrate the
superiority of our ULGF approach.
