# Two Local Models for Neural Constituent Parsing

arXiv: https://arxiv.org/abs/1808.04850 · HTML: https://mlsanity.com/paper/1808.04850/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [Investigating Non-local Features for Neural Constituency Parsing](https://mlsanity.com/paper/2109.12814/) (2021)
- [Discontinuous Grammar as a Foreign Language](https://mlsanity.com/paper/2110.10431/) (2021)
- [In-Order Chart-Based Constituent Parsing](https://mlsanity.com/paper/2102.04065/) (2021)
- [Order-sensitive Neural Constituency Parsing](https://mlsanity.com/paper/2211.00421/) (2022)
- [Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing](https://mlsanity.com/paper/2505.20976/) (2025)

## Abstract (arXiv)

Non-local features have been exploited by syntactic parsers for capturing dependencies between sub output structures. Such features have been a key to the success of state-of-the-art statistical parsers. With the rise of deep learning, however, it has been shown that local output decisions can give highly competitive accuracies, thanks to the power of dense neural input representations that embody global syntactic information. We investigate two conceptually simple local neural models for constituent parsing, which make local decisions to constituent spans and CFG rules, respectively. Consistent with previous findings along the line, our best model gives highly competitive results, achieving the labeled bracketing F1 scores of 92.4% on PTB and 87.3% on CTB 5.1.