SciREL at SemEval-2018 Task 7: A System for Semantic Relation Extraction and Classification
Year: 2018
Type: conference-paper
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 featurevector 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 Fmeasure of 20.03 on the official test corpus 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.
DOI: 10.18653/v1/s18-1137
Institution: Virginia Commonwealth University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2805508642