# Getting More Out Of Syntax with PropS

arXiv: https://arxiv.org/abs/1603.01648 · HTML: https://mlsanity.com/paper/1603.01648/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2016.

## Built on by (most cited first, 8 of 8)

- [DeepStruct: Pretraining of Language Models for Structure Prediction](https://mlsanity.com/paper/2205.10475/) (2022)
- [Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge](https://mlsanity.com/paper/2104.06828/) (2021)
- [Zero-Shot Information Extraction as a Unified Text-to-Triple Translation](https://mlsanity.com/paper/2109.11171/) (2021)
- [Syntactic and Semantic-driven Learning for Open Information Extraction](https://mlsanity.com/paper/2103.03448/) (2021)
- [IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models](https://mlsanity.com/paper/2210.14128/) (2022)
- [Context-Preserving Text Simplification](https://mlsanity.com/paper/2105.11178/) (2021)
- [Collaborative Knowledge Graph Fusion by Exploiting the Open Corpus](https://mlsanity.com/paper/2206.07472/) (2022)
- [Knowledge Bases in Support of Large Language Models for Processing Web News](https://mlsanity.com/paper/2411.08278/) (2024)

## Abstract (arXiv)

Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dependency trees, often leading to further ad-hoc heuristic post-processing or to information loss. To directly address the needs of semantic applications, we present PropS -- an output representation designed to explicitly and uniformly express much of the proposition structure which is implied from syntax, and an associated tool for extracting it from dependency trees.