###### Abstract  
Joint relational triple extraction is a crucial step in constructing a knowledge graph from unstructured text. Recently, multiple methods have been proposed for extracting relationship triplets. Notably, end-to-end table-filling methods have garnered significant research interest due to their efficient extraction capabilities. However, existing approaches usually generate separate tables for each relationship, which neglects the global correlation between relationships and context, producing a large number of useless blank tables. This problem results in issues of redundant information and sample imbalance. To address these challenges, we propose a novel framework for joint entity and relation extraction based on a single-table filling method. This method incorporates all relationships as prompts within the text sequence and associates entity span information with relationship labels. This approach reduces the generation of redundant information and enhances the extraction capability for overlapping triplets. We utilize the internal and external multi-head tensor fusion approach to generate two sets of table feature vectors. These vectors are subsequently merged to capture a wider range of global information. Experimental results on the NYT and WebNLG datasets demonstrate the effectiveness of our proposed model, which maintains excellent performance, even in complex scenarios involving overlapping triplets. joint entity and relation extraction; table filling; deep learning models; prompt learning; natural language processing +
Footnote †: journal:containing overlapping triplets. For instance, [10] divided joint extraction into three parts and completed the extraction in one stage, thereby avoiding exposure bias. Due to the complexity of human language, the number of entities and their relationships in a sentence is uncertain, which increases the difficulty of extracting relationship triplets [13], specifically, the problem of relationship overlap [14]. The overlapping problem refers to situations where multiple triplets share common entities or relations. Specifically, it involves cases where the boundaries of entities or relations overlap, creating ambiguity in the extraction process. The cases of triplet overlap can generally be classified into three categories: SEO, EPO, and SOO, as shown in Figure 1. If several triples in a sentence share the same entity, it is SEO; for example, the triples ([PERSON], graduated, University of London) and (England, contains, University of London) share the entity \"University of London\". It is EPO if there are multiple relationships between certain entity pairs; for example, the triples (France, contains, Paris) and (Paris, capital, France) both exist for the same entity pair \"France\" and \"Paris\". A triple is SOO if the subject and object are nested entities of each other; for example, if the object \"[PERSON]\" in the triple ([PERSON], first name, [PERSON]) is a nested entity of the subject \"[PERSON]\". The design of table-filling schemes also affects the ability to extract complex entities and relationships. Designing an efficient and highly generalizable table-filling method has become a research focus. Currently, models based on table-filling methods for extracting triplets usually maintain a separate table for each relationship, where each entry in the table represents the existence of a certain relationship between token pairs [11; 12]. However, this approach presents three issues: (1) It generates many useless relationship tables, and the number of positive samples in the tables with relationships is much smaller than the number of blank labels, making the model easily affected by negative samples during the learning process. (2) When extracting entity spans, it often relies on complex multi-class labels or additional tables to map the head and tail markers. The former requires a complex decoding mechanism to achieve triplet extraction, whereas the latter introduces more redundant information.