SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification

Darshini Mahendran,Bridget Mcinnes,Chathurika Brahmana

doi:10.18653/v1/s18-1137

Abstract

This paper describes our system, SciREL (Scientific abstract RELation extraction system), developed for the SemEval 2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers. We present a feature-vector based system to extract explicit semantic relation and classify them. Our system is trained in the ACL corpus (BIrd et al., 2008) that contains annotated abstracts given by the task organizers. When an abstract with annotated entities is given as the input into our system, it extracts the semantic relations through a set of defined features and classifies them into one of the given six categories of relations through feature engineering and a learned model. For the best combination of features, our system SciREL obtained an F-measure of 20.03 on the official test corpus which includes 150 abstracts in the relation classification Subtask 1.1. In this paper, we provide an in-depth error analysis of our results to prevent duplication of research efforts in the development of future systems

Highlights

Automatic detection and extraction of semantic relations among the entities from unstructured text has received growing attention in the recent years (Konstantinova, 2014), (Augenstein et al, 2017), (Fundel et al, 2006), (Luo et al, 2016)
Text mining is the process of automatically extracting knowledge from unstructured text documents and this idea of text mining is to link extracted information together which possibly results in new facts or hypothesis to be explored further through conventional scientific experimentations (Delen and Crossland, 2008), (Fleuren and Alkema, 2015)
Their tasks focus on identifying pairs of entities that are instances of six semantic relation types and classifying those instances into one of the six semantic relation types

Summary

Introduction

Automatic detection and extraction of semantic relations among the entities from unstructured text has received growing attention in the recent years (Konstantinova, 2014), (Augenstein et al, 2017), (Fundel et al, 2006), (Luo et al, 2016). SemEval 2018 Task 7 (Gabor et al, 2018) aims to extract and classify semantic relations to improve the access to scientific literature. Their tasks focus on identifying pairs of entities that are instances of six semantic relation types and classifying those instances into one of the six semantic relation types. To address this challenge, we implemented a supervised machine learning based approach in order to extract explicit semantic relations from the ACL anthology corpus (Bird et al, 2008) for Subtask 1.1

Objectives

Methods

Results

Conclusion