  Constituency parsing and nested named entity recognition (NER) are similar
tasks since they both aim to predict a collection of nested and non-crossing
spans. In this work, we cast nested NER to constituency parsing and propose a
novel pointing mechanism for bottom-up parsing to tackle both tasks. The key
idea is based on the observation that if we traverse a constituency tree in
post-order, i.e., visiting a parent after its children, then two consecutively
visited spans would share a boundary. Our model tracks the shared boundaries
and predicts the next boundary at each step by leveraging a pointer network. As
a result, it needs only linear steps to parse and thus is efficient. It also
maintains a parsing configuration for structural consistency, i.e., always
outputting valid trees. Experimentally, our model achieves the state-of-the-art
performance on PTB among all BERT-based models (96.01 F1 score) and competitive
performance on CTB7 in constituency parsing; and it also achieves strong
performance on three benchmark datasets of nested NER: ACE2004, ACE2005, and
GENIA. Our code is publicly available at
\url{https://github.com/sustcsonglin/pointer-net-for-nested}.
