## 2 Related Work
These methods perform the parallel recognition of entities and relations, reducing inter-step dependencies and, thus, minimizing the risk of exposure bias. [PERSON] et al. (2019) proposed a model called PRGC, which is based on sequence labeling. The model extracts potential entities and relations in parallel using a global matrix and a latent relation matrix, and it then combines the extracted entities and relations to generate triplets in a single stage. [PERSON] et al. (2019) introduced a single-stage entity and relation extraction framework, OneRel, which converts the task into a table-filling problem using a corner-tagging strategy, enabling the complete extraction of entities and relations in a single step. [PERSON] et al. (2019) proposed a single-stage joint extraction model called TPLinker, which is based on handshake tagging. The model extracts head and tail entities through token pair prediction and iterates over all relation combinations to generate triplets, mitigating exposure bias. Although the aforementioned methods avoid exposure bias by extracting entities and relations in parallel, they face the problem of redundant computation due to entity and relation recognition modules' separation when combining entities and relations. Moreover, these methods rely solely on semantic information, making it difficult to effectively capture the complex logical structures present in the text. With the introduction of syntactic information and graph neural networks, the model's ability to analyze complex textual features has significantly improved, leading to substantial progress in entity and relation extraction tasks. [PERSON] et al. (2019) proposed a multi-stage model called GraphRel. In the first stage (GraphRel1p), a BiRNN and GCN are used to extract sequence features and local dependency features to predict entity spans and relationships between each token pair. In the second stage (GraphRel2p), the predicted results derived from the first stage are used to construct a relationship-weighted graph for each relationship, and a BiGCN is employed to further integrate information for each relationship, enhancing the text's feature representation capability and improving overlapping triplet identification. [PERSON] et al. (2019) employed a two-stage approach combining graph attention networks and semantic dependency analysis, further improving the model's understanding of complex texts and enhancing extraction quality. [PERSON] et al. (2019) introduced a joint extraction model that integrates adversarial training, graph attention networks, and dependency analysis. By combining sequential structural features with words' semantic features, this approach effectively resolves overlapping triplets. The aforementioned methods, by incorporating syntactic information, enhance the model's ability to represent complex texts, improving extraction accuracy and the handling of overlapping triplets. However, due to the interdependence between entity and relation recognition, they are still at risk of exposure bias. In recent years, researchers have applied joint entity and relation extraction techniques to the agricultural domain, significantly improving the efficiency of agricultural knowledge utilization. [PERSON] et al. (2019) designed a joint extraction model based on dependency syntax analysis to address data dispersion and reuse difficulties with regard to agricultural product standards. The model automatically extracts knowledge in this field by leveraging an entity-relation extraction paradigm and syntactic analysis results. [PERSON] et al. (2019) used RoBERTa for semantic feature extraction, combining it with an improved CasRel model to extract entities and relations in multiple stages, achieving rice fertilization information extraction and integration and effectively resolving overlapping triplets in this domain. [PERSON] et al. (2019) designed a synchronized entity-relation tagging scheme to tackle overlapping triplets and knowledge-sharing difficulties in the wheat pest and disease domain.