Abstract: Entity relation extraction aims to extract semantic relations between entities from text.Up to now,related work on entity relation extraction mainly focuses on written texts,such as news and Wikipedia text,and has achieved considerable success.However,the research for dialogue texts is still in initial stage.At present,the dialogue corpus used for entity relation extraction is small in scale and low in information density,so it is difficult to capture effective features.The deep learning model does not associate knowledge like human beings,so it is difficult to understand the dialogue content in detail and depth simply by increasing the amount of annotation data and enhancing the computing power.In response to the above problems,this paper proposes a knowledge-integrated entity relation extraction model,which uses Star-Transformer to effectively capture features from dialogue texts,and constructs a relation set containing relations and their semantic keywords through the co-occurrence of keywords.The important relation features obtained by calculating the correlation between the set and dialogue text are integrated into the model as knowledge.Experiment results on the DialogRE dataset show that the F₁ value is 53.6% and the F_1c value is 49.5%,which proves the effectiveness of proposed method.

Key words: Dialogue context, Entity relation extraction, Transformer, Attention mechanism, Integrate knowledge