DeepAI AI Chat
Log In Sign Up

Towards Time-Aware Distant Supervision for Relation Extraction

by   Tianwen Jiang, et al.
Harbin Institute of Technology

Distant supervision for relation extraction heavily suffers from the wrong labeling problem. To alleviate this issue in news data with the timestamp, we take a new factor time into consideration and propose a novel time-aware distant supervision framework (Time-DS). Time-DS is composed of a time series instance-popularity and two strategies. Instance-popularity is to encode the strong relevance of time and true relation mention. Therefore, instance-popularity would be an effective clue to reduce the noises generated through distant supervision labeling. The two strategies, i.e., hard filter and curriculum learning are both ways to implement instance-popularity for better relation extraction in the manner of Time-DS. The curriculum learning is a more sophisticated and flexible way to exploit instance-popularity to eliminate the bad effects of noises, thus get better relation extraction performance. Experiments on our collected multi-source news corpus show that Time-DS achieves significant improvements for relation extraction.


page 1

page 2

page 3

page 4


Denoising Distant Supervision for Relation Extraction via Instance-Level Adversarial Training

Existing neural relation extraction (NRE) models rely on distant supervi...

DIAG-NRE: A Deep Pattern Diagnosis Framework for Distant Supervision Neural Relation Extraction

Modern neural network models have achieved the state-of-the-art performa...

Finding Influential Instances for Distantly Supervised Relation Extraction

Distant supervision has been demonstrated to be highly beneficial to enh...

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...

RDSGAN: Rank-based Distant Supervision Relation Extraction with Generative Adversarial Framework

Distant supervision has been widely used for relation extraction but suf...

Improving Reinforcement Learning for Neural Relation Extraction with Hierarchical Memory Extractor

Distant supervision relation extraction (DSRE) is an efficient method to...

Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks

We present a generic and trend-aware curriculum learning approach for gr...