Multi-level semantic representation enhancement network for relationship extraction

Discovering the relationship between two entities in a sentence is one of the most important tasks in natural language processing. Due to the superior feature learning capacity, deep learning-based relationship extraction methods have become prevailing. However, most of them rely on the features provided by tools such as dependency parser, part-of-speech tagger, or named entity recognizers. Moreover, although models such as convolutional neural networks or recurrent neural networks have their merits in text feature extraction, the semantic representation of entities’ relationships are still far from optimal. To address this issue, this paper proposes a novel end-to-end multi-level semantic representation enhancement network (MLSREN). MLSREN can enhance the semantic representation of entities from word, phrase, and context level. Furthermore, to extract deeper semantic information of phrases with fewer network parameters, a LightText-CNN is proposed for local feature extraction. Extensive experiments are conducted on the SemEval-2010 task 8 dataset, the results demonstrate that our model significantly improves state-of-the-art performance. To better understand performance achieved by MLSREN, we also analyze the contribution of each network component as well as the problems in the feature selection of the current relationship extraction models.