End-to-End Relation Extraction using Markov Logic Networks
  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. %Identifying entity mentions along
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.
