Jointly Extracting Relations with Class Ties via Effective Deep Ranking

12/22/2016
by   Hai Ye, et al.
0

Connections between relations in relation extraction, which we call class ties, are common. In distantly supervised scenario, one entity tuple may have multiple relation facts. Exploiting class ties between relations of one entity tuple will be promising for distantly supervised relation extraction. However, previous models are not effective or ignore to model this property. In this work, to effectively leverage class ties, we propose to make joint relation extraction with a unified model that integrates convolutional neural network (CNN) with a general pairwise ranking framework, in which three novel ranking loss functions are introduced. Additionally, an effective method is presented to relieve the severe class imbalance problem from NR (not relation) for model training. Experiments on a widely used dataset show that leveraging class ties will enhance extraction and demonstrate the effectiveness of our model to learn class ties. Our model outperforms the baselines significantly, achieving state-of-the-art performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/25/2019

Deep Ranking Based Cost-sensitive Multi-label Learning for Distant Supervision Relation Extraction

Knowledge base provides a potential way to improve the intelligence of i...
research
09/10/2021

D-REX: Dialogue Relation Extraction with Explanations

Existing research studies on cross-sentence relation extraction in long-...
research
11/15/2019

DNNRE: A Dynamic Neural Network for Distant Supervised Relation Extraction

Distant Supervised Relation Extraction (DSRE) is usually formulated as a...
research
05/24/2023

ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction

A plethora of approaches have been proposed for joint entity-relation (E...
research
04/21/2020

Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction

Relation ties, defined as the correlation and mutual exclusion between d...
research
09/01/2022

Less is More: Rethinking State-of-the-art Continual Relation Extraction Models with a Frustratingly Easy but Effective Approach

Continual relation extraction (CRE) requires the model to continually le...
research
03/20/2021

Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction

We study the problem of entity-relation extraction in the presence of sy...

Please sign up or login with your details

Forgot password? Click here to reset