基于双仿射注意力的迭代式开放域信息抽取OA北大核心CSTPCD

The current OpenIE methods cannot take into account the compactness of the extraction results and the performance of the model at the same time,which makes the extraction results unable to be better applied to downstream tasks.Therefore,this paper proposed a model that used biaffine attention for table filling and iterative extraction.Firstly,the model learned the directional information between words through biaffine attention,captured the interaction between word pairs,and then filled the two-dimensional table to make the components in the sentence share each other and identify compact components.Second-ly,it used the multi-head attention mechanism to apply the representation of predicates and parameters to the context embed-ding,making the extraction of predicates and parameters dependent on each other and better linking the relationship compo-nents and parameter components.Finally,for sentences containing multiple relational components,it used iterative extraction to capture the inherent dependencies between each extraction without recoding.Experiments on the public datasets CaRB and Wire57 show that this method achieves higher precision and recall than baseline methods,improving F1 values by at least 1.4％and 3.2％,while producing shorter and semantically richer extractions.