# About Evaluation of F1 Score for RECENT Relation Extraction System

arXiv: https://arxiv.org/abs/2305.09410 · HTML: https://mlsanity.com/paper/2305.09410/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 16 May 2023.

## Builds on (most cited first, 6 of 6)

- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [An Improved Baseline for Sentence-level Relation Extraction](https://mlsanity.com/paper/2102.01373/) (2021)
- [Relation Classification with Entity Type Restriction](https://mlsanity.com/paper/2105.08393/) (2021)
- [Relation Classification as Two-way Span-Prediction](https://mlsanity.com/paper/2010.04829/) (2020)
- [Improving Sentence-Level Relation Extraction through Curriculum Learning](https://mlsanity.com/paper/2107.09332/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Grounded and Decomposed Framework for Relation-Level Hallucination Evaluation in Abstractive Summarization](https://mlsanity.com/paper/2608.08180/) (2026)

## Abstract (arXiv)

This document contains a discussion of the F1 score evaluation used in the article 'Relation Classification with Entity Type Restriction' by Shengfei Lyu, Huanhuan Chen published on Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. The authors created a system named RECENT and claim it achieves (then) a new state-of-the-art result 75.2 (previous 74.8) on the TACRED dataset, while after correcting errors and reevaluation the final result is 65.16