An Improved Baseline for Sentence-level Relation Extraction

02/02/2021
by   Wenxuan Zhou, et al.
0

Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. Many efforts have been devoted to this problem, while the best performing methods are still far behind human performance. In this paper, we revisit two aspects of RE models that are not thoroughly studied, namely entity representation and NA instance prediction. Our improved baseline model, incorporated with entity representations with type markers and confidence-based classification for enhanced NA instance detection, achieves an F1 of 75.0 methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/30/2023

iMETRE: Incorporating Markers of Entity Types for Relation Extraction

Sentence-level relation extraction (RE) aims to identify the relationshi...
research
05/01/2018

Joint Bootstrapping Machines for High Confidence Relation Extraction

Semi-supervised bootstrapping techniques for relationship extraction fro...
research
09/18/2020

RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network

In this paper, we present a novel method named RECON, that automatically...
research
02/19/2019

A Walk-based Model on Entity Graphs for Relation Extraction

We present a novel graph-based neural network model for relation extract...
research
04/17/2020

Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction

Despite the recent progress, little is known about the features captured...
research
05/08/2022

GRAPHCACHE: Message Passing as Caching for Sentence-Level Relation Extraction

Entity types and textual context are essential properties for sentence-l...
research
06/15/2023

Rethinking Document-Level Relation Extraction: A Reality Check

Recently, numerous efforts have continued to push up performance boundar...

Please sign up or login with your details

Forgot password? Click here to reset