  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.
