  Nested named entity recognition (NER) has been receiving increasing
attention. Recently, (Fu et al, 2021) adapt a span-based constituency parser to
tackle nested NER. They treat nested entities as partially-observed
constituency trees and propose the masked inside algorithm for partial
marginalization. However, their method cannot leverage entity heads, which have
been shown useful in entity mention detection and entity typing. In this work,
we resort to more expressive structures, lexicalized constituency trees in
which constituents are annotated by headwords, to model nested entities. We
leverage the Eisner-Satta algorithm to perform partial marginalization and
inference efficiently. In addition, we propose to use (1) a two-stage strategy
(2) a head regularization loss and (3) a head-aware labeling loss in order to
enhance the performance. We make a thorough ablation study to investigate the
functionality of each component. Experimentally, our method achieves the
state-of-the-art performance on ACE2004, ACE2005 and NNE, and competitive
performance on GENIA, and meanwhile has a fast inference speed.
