Neural Architectures for Open-Type Relation Argument Extraction
Abstract
In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e.g.,"Q authored notable work with title X"), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, e.g. X: the title of a book or a work of art) from the corpus. A distantly supervised dataset based on WikiData relations is obtained and released to address the task. We develop and compare a wide range of neural models for this task yielding large improvements over a strong baseline obtained with a neural question answering system. The impact of different sentence encoding architectures and answer extraction methods is systematically compared. An encoder based on gated recurrent units combined with a conditional random fields tagger gives the best results.
Cite
@article{arxiv.1803.01707,
title = {Neural Architectures for Open-Type Relation Argument Extraction},
author = {Benjamin Roth and Costanza Conforti and Nina Poerner and Sanjeev Karn and Hinrich Schütze},
journal= {arXiv preprint arXiv:1803.01707},
year = {2019}
}