SENT: Sentence-level Distant Relation Extraction via Negative Training

06/22/2021
by   Ruotian Ma, et al.
0

Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications that need the exact relation type. Directly using bag labels for sentence-level training will introduce much noise, thus severely degrading performance. In this work, we propose the use of negative training (NT), in which a model is trained using complementary labels regarding that “the instance does not belong to these complementary labels". Since the probability of selecting a true label as a complementary label is low, NT provides less noisy information. Furthermore, the model trained with NT is able to separate the noisy data from the training data. Based on NT, we propose a sentence-level framework, SENT, for distant relation extraction. SENT not only filters the noisy data to construct a cleaner dataset, but also performs a re-labeling process to transform the noisy data into useful training data, thus further benefiting the model's performance. Experimental results show the significant improvement of the proposed method over previous methods on sentence-level evaluation and de-noise effect.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/22/2019

Are Noisy Sentences Useless for Distant Supervised Relation Extraction?

The noisy labeling problem has been one of the major obstacles for dista...
research
03/30/2019

Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions

This paper presents a neural relation extraction method to deal with the...
research
06/21/2021

CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction

The journey of reducing noise from distant supervision (DS) generated tr...
research
04/28/2020

A Practical Framework for Relation Extraction with Noisy Labels Based on Doubly Transitional Loss

Either human annotation or rule based automatic labeling is an effective...
research
04/17/2019

Posterior-regularized REINFORCE for Instance Selection in Distant Supervision

This paper provides a new way to improve the efficiency of the REINFORCE...
research
01/04/2016

Distant IE by Bootstrapping Using Lists and Document Structure

Distant labeling for information extraction (IE) suffers from noisy trai...
research
05/24/2018

DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction

Distant supervision can effectively label data for relation extraction, ...

Please sign up or login with your details

Forgot password? Click here to reset