# A Sentence Simplification System for Improving Relation Extraction

arXiv: https://arxiv.org/abs/1703.09013 · HTML: https://mlsanity.com/paper/1703.09013/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification](https://mlsanity.com/paper/2310.08170/) (2023)
- [A New Aligned Simple German Corpus](https://mlsanity.com/paper/2209.01106/) (2022)
- [How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks](https://mlsanity.com/paper/2109.04604/) (2021)
- [Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation](https://mlsanity.com/paper/2404.03278/) (2024)
- [Context-Aware Document Simplification](https://mlsanity.com/paper/2305.06274/) (2023)
- [Mitigating Data Scarcity for Large Language Models](https://mlsanity.com/paper/2302.01806/) (2023)
- [LLM-based Atomic Propositions help weak extractors: Evaluation of a Propositioner for triplet extraction](https://mlsanity.com/paper/2604.02866/) (2026)

## Abstract (arXiv)

In this demo paper, we present a text simplification approach that is directed at improving the performance of state-of-the-art Open Relation Extraction (RE) systems. As syntactically complex sentences often pose a challenge for current Open RE approaches, we have developed a simplification framework that performs a pre-processing step by taking a single sentence as input and using a set of syntactic-based transformation rules to create a textual input that is easier to process for subsequently applied Open RE systems.