## 1 Introduction  
Automatically extracting relational quintuples is a typical task in information extraction, which is widely used in vertical domains. Especially in various specialized fields, it can help nonspecialists to understand how cleaning evolved and remains relevant. Quintuples are considered meta-information for knowledge-base construction, including the type of head entity, the name of the head entity, the type of relation, the type of tail entity, and the name of the tail entity. This kind of information can form a knowledge graph by the merging of the same entity and relation, such as the vertex and the edge. This task has been labeled \"Rel+\" in some papers, in contrast to the entity-relationship joint extraction task, which does not consider unrelated entities when extracting quintuples. This can be seen in the recent datasets, such as scientific papers [1], the financial domain [2], biomedical information [3], and energy data [4]. Feature words play an important role in the relation quintuple extraction task. These words act as triggers to guide relational ternary extraction [5]. We define a feature word as a key word that assists in the extraction of relational quintuples and establishes the relevance of the original sentence after the construction of a knowledge graph. Thus, the feature words decide which contribute to the specific type and act as a kind of relation prompt. These feature words are added to the knowledge graph interface to increase the degree of connectivity, allowing users to obtain more relevant information when querying the knowledge graph. Thus, the task is defined as the extraction of relational quintuples and feature words from each sentence in the dataset. Similar to this idea, [PERSON] et al. [6] used feature words as boundaries to restrict the position of entities during extraction. However, these labeling processes consume resources due to boundary labeling. [PERSON] et al. [7] used feature words for relational ternary extraction in the urban rail domain, but they still used the hidden semantics of the feature words without feature word labeling, instead of outputting the feature words themselves with the supervision method. As a result, there was no improvement in graph connectivity at the time of graph construction. From the above, it is known that the feature words can provide self-enhancing information for the extraction process. However, since these feature words are unlabeled, we consider using \"associative supervision\" [8] to extract these words during quintuple extraction, unlike existing semi-supervised schemes. Using such a supervised method, we are the first to propose the direct output of feature words. The word-based approach provides a model design perspective that influences the relationship classification results. Because of the pre-trained model and the large language model, the semantic features are no longer the bottleneck; therefore, it is very important to retrieve the text expression or separate the targets from the sentence. In [PERSON]'s work, the relation results of the expression and the predefined templates were encoded to generate a fine-grained semantic representation [9], which injects more link features into the training model. Learning the features from existing results [10] can generate global features and enrich the pattern information in the current sample. In addition, when the training data change and the head and tail entities are arranged in order and disorder, the ordering of the two entities in the quintuple can be learned [11]. Remarkable improvements in the word-based solution show that both the modified model and the refined data are required. This approach can be of use in the extraction of feature words in conjunction with relational quintuples. Despite its success in using words, this approach still has a number of shortcomings in setting fixed words as feature words. The fixed words, regarded as templates, are not flexible enough in their meaning, which may result in redundant or missing information.