# Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs

arXiv: https://arxiv.org/abs/1606.08954 · HTML: https://mlsanity.com/paper/1606.08954/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [Autoregressive Structured Prediction with Language Models](https://mlsanity.com/paper/2210.14698/) (2022)
- [Joint Universal Syntactic and Semantic Parsing](https://mlsanity.com/paper/2104.05696/) (2021)

## Abstract (arXiv)

We present a transition-based parser that jointly produces syntactic and semantic dependencies. It learns a representation of the entire algorithm state, using stack long short-term memories. Our greedy inference algorithm has linear time, including feature extraction. On the CoNLL 2008--9 English shared tasks, we obtain the best published parsing performance among models that jointly learn syntax and semantics.