End-to-End Relation Extraction using Markov Logic Networks

by   Sachin Pawar, et al.

The task of end-to-end relation extraction consists of two sub-tasks: i) identifying entity mentions along with their types and ii) recognizing semantic relations among the entity mention pairs. with their types and recognizing semantic relations among the entity mentions, are two very important problems in Information Extraction. It has been shown that for better performance, it is necessary to address these two sub-tasks jointly. We propose an approach for simultaneous extraction of entity mentions and relations in a sentence, by using inference in Markov Logic Networks (MLN). We learn three different classifiers : i) local entity classifier, ii) local relation classifier and iii) "pipeline" relation classifier which uses predictions of the local entity classifier. Predictions of these classifiers may be inconsistent with each other. We represent these predictions along with some domain knowledge using weighted first-order logic rules in an MLN and perform joint inference over the MLN to obtain a global output with minimum inconsistencies. Experiments on the ACE (Automatic Content Extraction) 2004 dataset demonstrate that our approach of joint extraction using MLNs outperforms the baselines of individual classifiers. Our end-to-end relation extraction performance is better than 2 out of 3 previous results reported on the ACE 2004 dataset.


page 1

page 2

page 3

page 4


Techniques for Jointly Extracting Entities and Relations: A Survey

Relation Extraction is an important task in Information Extraction which...

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

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

Encoding Implicit Relation Requirements for Relation Extraction: A Joint Inference Approach

Relation extraction is the task of identifying predefined relationship b...

Integrating Deep Learning with Logic Fusion for Information Extraction

Information extraction (IE) aims to produce structured information from ...

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...

Integrating Relation Constraints with Neural Relation Extractors

Recent years have seen rapid progress in identifying predefined relation...

Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification

We introduce globally normalized convolutional neural networks for joint...

Please sign up or login with your details

Forgot password? Click here to reset