# Constituent Parsing as Sequence Labeling

arXiv: https://arxiv.org/abs/1810.08994 · HTML: https://mlsanity.com/paper/1810.08994/

## Key facts

- Cited by 26 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 21 Oct 2018.

## Built on by (most cited first, 26 of 26)

- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Bottom-Up Constituency Parsing and Nested Named Entity Recognition with Pointer Networks](https://mlsanity.com/paper/2110.05419/) (2021)
- [4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees](https://mlsanity.com/paper/2310.14319/) (2023)
- [A Conditional Splitting Framework for Efficient Constituency Parsing](https://mlsanity.com/paper/2106.15760/) (2021)
- [Discontinuous Grammar as a Foreign Language](https://mlsanity.com/paper/2110.10431/) (2021)
- [Structure-Unified M-Tree Coding Solver for MathWord Problem](https://mlsanity.com/paper/2210.12432/) (2022)
- [Syntactic Control of Language Models by Posterior Inference](https://mlsanity.com/paper/2506.07154/) (2025)
- [Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective](https://mlsanity.com/paper/2305.15057/) (2023)
- [Assessment of Pre-Trained Models Across Languages and Grammars](https://mlsanity.com/paper/2309.11165/) (2023)
- [On the Challenges of Fully Incremental Neural Dependency Parsing](https://mlsanity.com/paper/2309.16254/) (2023)
- [A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis](https://mlsanity.com/paper/2406.15163/) (2024)
- [Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSA](https://mlsanity.com/paper/2406.16071/) (2024)
- [Dependency Graph Parsing as Sequence Labeling](https://mlsanity.com/paper/2410.17972/) (2024)
- [Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs](https://mlsanity.com/paper/2506.09983/) (2025)
- [In-Order Chart-Based Constituent Parsing](https://mlsanity.com/paper/2102.04065/) (2021)
- [Neural Combinatory Constituency Parsing](https://mlsanity.com/paper/2106.06689/) (2021)
- [Splitting EUD graphs into trees: A quick and clatty approach](https://mlsanity.com/paper/2106.13155/) (2021)
- [Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing](https://mlsanity.com/paper/2212.08458/) (2022)
- [From Partial to Strictly Incremental Constituent Parsing](https://mlsanity.com/paper/2402.02782/) (2024)
- [Bregman Conditional Random Fields: Sequence Labeling with Parallelizable Inference Algorithms](https://mlsanity.com/paper/2506.00732/) (2025)
- [Bringing Emerging Architectures to Sequence Labeling in NLP](https://mlsanity.com/paper/2509.25918/) (2025)
- [Joint Chinese Word Segmentation and Span-based Constituency Parsing](https://mlsanity.com/paper/2211.01638/) (2022)
- [On Parsing as Tagging](https://mlsanity.com/paper/2211.07344/) (2022)
- [Nested Named Entity Recognition as Single-Pass Sequence Labeling](https://mlsanity.com/paper/2505.16855/) (2025)
- [Hierarchical Bracketing Encodings Work for Dependency Graphs](https://mlsanity.com/paper/2509.09388/) (2025)
- [Multi-Task Learning by using Contextualized Word Representations for Syntactic Parsing of a Morphologically Rich Language](https://mlsanity.com/paper/2609.29855/) (2026)

## Abstract (arXiv)

We introduce a method to reduce constituent parsing to sequence labeling. For each word w_t, it generates a label that encodes: (1) the number of ancestors in the tree that the words w_t and w_{t+1} have in common, and (2) the nonterminal symbol at the lowest common ancestor. We first prove that the proposed encoding function is injective for any tree without unary branches. In practice, the approach is made extensible to all constituency trees by collapsing unary branches. We then use the PTB and CTB treebanks as testbeds and propose a set of fast baselines. We achieve 90.7% F-score on the PTB test set, outperforming the Vinyals et al. (2015) sequence-to-sequence parser. In addition, sacrificing some accuracy, our approach achieves the fastest constituent parsing speeds reported to date on PTB by a wide margin.