Neural Open Relation Extraction via an Overlap-aware Sequence Tagging Scheme

Solving the Open relation extraction (ORE) task with supervised neural networks, especially the neural sequence learning (NSL) models, is an extraordinarily promising way. However, there are three main challenges: (1) The lack of labeled training corpus; (2) Only one label is assigned to each word, resulting in being difficult to extract multiple, overlapping relations; (3) The confusion about the selection of various neural architectures for the ORE. In this paper, to overcome these challenges, we design a novel tagging scheme to assist in building a large-scale, high-quality training dataset automatically. The scheme can improve the performance of models by assigning multiple, overlapping labels for each word and helping models to learn pre-identifying arguments segment-level information. In addition, we pick out a winning model empirically from various alternative neural structures. The model achieves state-of-the-art performance on four kinds of test sets. The experimental results show that the scheme is effective.