Improving Distant Supervised Relation Extraction by Dynamic Neural Network
Table 1: The sample numbers of different relation classes in the NYT training set, which is pretty class-imbalanced: the samples of half relation classes is less than 100.
['realtion class', 'number of class samples']
keywords: Deep neural network, Distant supervision, Relation extraction, Dynamic paremater, Style shift, Long-tail relation †journal: Knowledge-Based Systems Relation Extraction (RE) Zelenko et al. et al. Due to the difficulty of collecting a large amount of sentence-level annotations, most recent RE methods are based on the Distant Supervision (DS) framework Mintz et al. With the DS framework, RE can be cast as a problem of classifying a bag of sentences which contain the same query entity pair, into predefined relation classes. However, DS framework has its own disadvantages, that is, the noise label and long-tail problems.