2 research outputs found
Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction
Relation ties, defined as the correlation and mutual exclusion between
different relations, are critical for distant supervised relation extraction.
Existing approaches model this property by greedily learning local
dependencies. However, they are essentially limited by failing to capture the
global topology structure of relation ties. As a result, they may easily fall
into a locally optimal solution. To solve this problem, in this paper, we
propose a novel force-directed graph based relation extraction model to
comprehensively learn relation ties. Specifically, we first build a graph
according to the global co-occurrence of relations. Then, we borrow the idea of
Coulomb's Law from physics and introduce the concept of attractive force and
repulsive force to this graph to learn correlation and mutual exclusion between
relations. Finally, the obtained relation representations are applied as an
inter-dependent relation classifier. Experimental results on a large scale
benchmark dataset demonstrate that our model is capable of modeling global
relation ties and significantly outperforms other baselines. Furthermore, the
proposed force-directed graph can be used as a module to augment existing
relation extraction systems and improve their performance.Comment: Learning Relation Tie
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework
Distant supervision for relation extraction enables one to effectively
acquire structured relations out of very large text corpora with less human
efforts. Nevertheless, most of the prior-art models for such tasks assume that
the given text can be noisy, but their corresponding labels are clean. Such
unrealistic assumption is contradictory with the fact that the given labels are
often noisy as well, thus leading to significant performance degradation of
those models on real-world data. To cope with this challenge, we propose a
novel label-denoising framework that combines neural network with probabilistic
modelling, which naturally takes into account the noisy labels during learning.
We empirically demonstrate that our approach significantly improves the current
art in uncovering the ground-truth relation labels.Comment: To appear in 2019 Conference on Empirical Methods in Natural Language
Processing and 9th International Joint Conference on Natural Language
Processin