  Named entity recognition (NER) is a fundamental task in natural language
processing. Recent works treat named entity recognition as a reading
comprehension task, constructing type-specific queries manually to extract
entities. This paradigm suffers from three issues. First, type-specific queries
can only extract one type of entities per inference, which is inefficient.
Second, the extraction for different types of entities is isolated, ignoring
the dependencies between them. Third, query construction relies on external
knowledge and is difficult to apply to realistic scenarios with hundreds of
entity types. To deal with them, we propose Parallel Instance Query Network
(PIQN), which sets up global and learnable instance queries to extract entities
from a sentence in a parallel manner. Each instance query predicts one entity,
and by feeding all instance queries simultaneously, we can query all entities
in parallel. Instead of being constructed from external knowledge, instance
queries can learn their different query semantics during training. For training
the model, we treat label assignment as a one-to-many Linear Assignment Problem
(LAP) and dynamically assign gold entities to instance queries with minimal
assignment cost. Experiments on both nested and flat NER datasets demonstrate
that our proposed method outperforms previous state-of-the-art models.
