Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction

Oscar Sainz, Oier Lopez de Lacalle, Itziar Aldabe, Montse Maritxalar


Abstract
In this paper we present a relation extraction system that given a text extracts pedagogically motivated relation types, as a previous step to obtaining a semantic representation of the text which will make possible to automatically generate questions for reading comprehension. The system maps pedagogically motivated relations with relations from ConceptNet and deploys Distant Supervision for relation extraction. We run a study on a subset of those relationships in order to analyse the viability of our approach. For that, we build a domain-specific relation extraction system and explore two relation extraction models: a state-of-the-art model based on transfer learning and a discrete feature based machine learning model. Experiments show that the neural model obtains better results in terms of F-score and we yield promising results on the subset of relations suitable for pedagogical purposes. We thus consider that distant supervision for relation extraction is a valid approach in our target domain, i.e. biology.
Anthology ID:
2020.lrec-1.270
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2213–2222
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.270
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.270.pdf