关键词: 关键词：实体关系抽取, 深度学习, 特定关系预测, 冗余计算, 全局指针标注策略

Abstract: Abstract: Entity-relationship extraction is an important part of artificial intelligence technologies such as building knowledge graphs and improving search engine efficiency. Due to the complexity， ambiguity， and implicit nature of Chinese text composition， the process of Chinese entity relationship extraction is prone to entity overlapping， entity nesting， and information redundancy. Therefore， this paper proposes a deep learning-based joint extraction model of Chinese entity relations（SRGP). The model firstly encodes the input text， obtains the set of specific relations through the specific relation prediction network， fuses the set of specific relations with the input text into the entity recognition module through the attention mechanism， and reduces the redundant computation in the extraction of Chinese entity relations. For the problems of insufficient extraction of overlapping entities and inaccurate recognition of nested entities， the global pointer annotation strategy based on specific relations is proposed by utilizing the idea of global normalization under the constraints of a specific set of relations. Two general Chinese datasets， DUIE1.0 and CMeIE， are selected respectively， and this paper’s model， SRGP， is compared with the typical models of entity-relationship joint extraction， such as CopyRE， PRGC， and CasRel， for the comparison experiments， and the experimental results show that this paper’s model achieves F1 values of 61.3% and 80.1% on the two datasets， which are respectively 1.5 and 2.2 percentage points higher than those of the best-performing baseline models CasRel and PRGC.

Key words: Key words: entity relationship extraction, deep learning, relationship-specific forecasting, redundant computing, global pointer labeling policy