Relation Extraction in Vietnamese Text via Piecewise Convolution Neural Network with Word-Level Attention

With the explosion of information technology, the Internet now contains enormous amounts of data, so the role of information extraction systems becomes very important. Relation Extraction is a sub-task of Information Extraction, which focuses on classifying the relationship between the entity pairs mentioned in the text. In recent years, despite the many new methods have been introduced, Relation Extraction still receives attention from researchers for languages in general and Vietnamese in particular.Relation Extraction can be addressed in a variety of ways, including supervised learning methods, unsupervised and semi-supervised methods. Recent studies in the English language have shown that Relation Extraction using deep learning method in the supervised or semi-supervised domains is achieving optimal and superior results over traditional non-deep learning methods. However, researches in Vietnamese are few and in the process of searching documents, the results of deep learning applying for Relation Extraction in Vietnamese are not found. Therefore, the research focuses on studying and research the method of using deep learning to solve Relation Extraction task in Vietnamese. In order to solve the Relation Extraction task, the research proposes and constructs a deep learning model named Piecewise Convolution Neural Network with Word-Level Attention.