# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 5 Conclusions  
In this work, we proposed a novel entity-relation extraction model aimed at addressing the common challenges in current entity-relation extraction tasks, such as insufficient semantic representations and the complexity of relationship types, which make triplet extraction more difficult. Specifically, to tackle the issue of insufficient semantic information extraction by existing models' encoders, we introduced dependency syntax information, providing critical semantic insights for the entity-relation extraction task. This enables the model to more accurately recognize and leverage syntactic dependencies within a sentence, enhancing its understanding of sentence structure and learning richer feature representations. In the relation classification layer, we incorporated a mixture-of-experts architecture, where a gating network dynamically generates different assignment weights for the experts, allowing the trained experts to capture specific types of relations or features, thereby improving the overall accuracy and generalization ability of the model. Through experiments on the DUIE and NYT datasets, we confirm that compared to baseline models, the proposed model demonstrates superior performance, with both the syntax information module and the mixture-of-experts architecture contributing to increased precision of entity-relation extraction. This study provides an effective approach for entity-relation extraction tasks and offers new perspectives for other similar natural language processing tasks. However, the proposed model still faces some limitations in terms of dataset diversity and computational cost. Its performance may vary across different domains and types of datasets. We could further expand the experimental scale to validate the model's reliability and practicality in subsequent research. In addition, although the model's accuracy improved, its computational cost remains high. We will consider optimizing the model's computational efficiency, for example, by using techniques such as pruning or knowledge distillation, to reduce the computational overhead. In future work, we will explore additional applicational scenarios for the proposed model. In the multimodal entity-relation extraction task, we aim to leverage the mixture-of-experts architecture to integrate information from diverse modalities. Each expert will specialize in processing a specific type of data, extracting richer semantic features from visual cues in images and audio characteristics like tone and speech rate, such as images and audio, to further enhance the accuracy and robustness of entity-relation extraction. Additionally, applications in low-resource environments are an important avenue for future research. Our model has shown promising results on two datasets with distinct language features, providing preliminary evidence of its cross-lingual generalization capability. By using dependency parsing to capture syntactic dependencies, the model is well-suited to handle structural differences across languages. The mixture-of-experts architecture enables the efficient selection and processing of different relational types, and its flexibility enhances the model's ability to generalize across diverse linguistic data. We could combine techniques such as prompt learning and domain adaptation to extend our model to low-resource or underrepresented languages, addressing the issue of limited annotated data while broadening its potential applications. Conceptualization, Y.L. ; formal analysis, Y.L. ; funding acquisition, H.W. and D.Z. ; investigation, Y.L. ; methodology, Y.L. ; project administration, H.W. and D.Z. ; software, Y.L. ; supervision, H.W. ; validation, Y.L. ; writing--original draft, Y.L. ; writing--review and editing, Y.L. and H.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the 5-5 Engineering Research & Innovation Team Project of Beijing Forestry University (No: BLRC2023C02). **Institutional Review Board Statement:** Not applicable.