A Joint Entity and Relation Extraction Model Based on Encoder-Decoder

Relation extraction is a task of automatically detecting and identifying predefined semantic relationships between identified entities in text. As a core and basic technology of knowledge acquisition in knowledge engineering, relation extraction endows artificial intelligence with strong ability of knowledge understanding. Massive text data, as the carrier of human knowledge, is rapidly submerged in the tide of information with the explosive growth of information. Mining knowledge hidden in these texts, is not only the theoretical demand of natural language processing but also the practical demand of human civilization inheritance. Natural language processing based on deep learning methods has made great progress in relation extraction field, effectively promoting knowledge discovering in texts of various granularity. However, some problems in relation extraction still need solving in the practical research process. In view of the existing work, most of the relations extraction task is divided into two independent sub-tasks, named entity recognition and relation classification, which lack the interaction between named entity recognition and relation classification in the sentence, and cannot handle the overlapping entity and relationship triples well. To solve these problems, a joint entity and relation extraction model based on Encoder-Decoder structure is proposed.