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