Research on Entity Attribute Extraction Combined with Attribute Semantics in Question Answering Mode
Entity attribute extraction tasks often face the problem of model explosion risk when there are too many attribute labels,and at present,most attribute extraction models assign consistent attention factors to texts,and do not take context changes into account.To solve the above problems,an entity attribute extraction method based on question-answering mode combined with attribute semantics is proposed.The key point of this method is that the text is regarded as the context,the attribute is regarded as the query,and the answer ex-tracted from the context is equivalent to the expected attribute value.The semantic representation of text and attributes is modeled,and a dynamic attention is proposed to capture the semantic interaction,realize information fusion,and adaptively control the degree to which at-tribute information is integrated into the text vector.In order to verify the effectiveness of the proposed method,the model was compared with the currently widely used models such as BiLSTM,BiLSTM-CRF,OpenTag and Open Tagging on datasets AE-110K and AE-650K containing a large number of attribute tags.It is showed that under the condition of combining attribute semantic information and a-dopting dynamic Attention,the model has higher prediction accuracy,recall rate and Fl value.
万方数据
维普
