A Pruning-Based Word-Centered Context Fragment Extraction Method for Relation Extraction

Neural relationship extraction is an important task in natural language processing, aimed at extracting relationships between target entity pairs from a given text. In recent years, with the development of deep neural networks, various types of neural networks to extract sentence entity-level, fragment-level, and sentence-level features for relationship extraction have become a mainstream research direction. Most existing studies use the BERT model to embed sentences and then use CNN to manipulate all words in the entire sentence to obtain fragment-level features. This article proposes a new word-centered context fragment-level method based on pruning the shortest dependency path between entity pairs. We demonstrate that using a pruning method based on the shortest dependency path between entity pairs can effectively improve the ability of model fragments and information extraction. We evaluated our method on a public benchmark: SemEval 2010 Task 8. The experimental results show that our method outperforms the advanced model using BERT as the embedding.