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

Darshini Mahendran, Chathurika Brahmana, Bridget McInnes


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
Anthology ID:
S18-1137
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
853–857
Language:
URL:
https://www.aclweb.org/anthology/S18-1137
DOI:
10.18653/v1/S18-1137
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1137.pdf