# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 2 Literature Review
### Recent Advances in Entity-Relation Extraction
#### 2.1.2 Joint Extraction Methods  
Since the annotated data used in relation extraction tasks originate from the previously named-entity recognition task, there is a certain correlation between the two tasks. However, in pipeline methods, the named-entity recognition task and the relation extraction task are learned and trained independently, which leads to a lack of interaction between the two subtasks, weakening the connection between them, causing underutilization of textual information, and resulting in issues like redundant entities. These issues lead to errors that, in turn, affect the quality of relation extraction results. Therefore, joint extraction methods have become mainstream in recent years, aiming to use the named-entity recognition task to better assist entity-relation extraction. [PERSON] and [PERSON] [12] first employed a neural network-based approach for joint extraction, capturing both word-sequence and dependency-tree-substructure information by stacking a bidirectional tree structure on a bidirectional LSTM-RNN. In this model, the sequence-labeling task and the prediction task are carried out simultaneously, and the predicted results are obtained through a decoder, with parameter updates performed during the training process using the backpropagation algorithm. [PERSON] et al. [13] proposed an architecture for joint entity and relation extraction, using external lexical and syntactic knowledge to address the limitations encountered by BERT-based models in joint extraction tasks within specific Chinese domains. [PERSON] et al. [14] proposed a cascade binary-labeling framework named CASREL for relation triplet extraction. The model first obtains the boundary information of the head entity via sequence labeling, and then, for each relation, a binary sequence labeler identifies the tail entity corresponding to the head entity in the word-relation pair, ultimately achieving joint entity-relation extraction. [PERSON] et al. [15] developed a fine-grained triplet classification model, OneRel, which follows a single-module, single-step modeling approach, directly extracting triplets from text statements, achieving performance superior to previous studies on public datasets. [PERSON] et al. [16] proposed a Universal Information Extraction (UIE) framework, which unifies the extraction of different structures by encoding structured extractioninformation and then adaptively generates target extractions based on a pattern extraction mechanism and a structure-guided controller. The aforementioned methods primarily utilize BERT as the pre-trained model, which is a typical representative of transformer-based approaches. However, pre-trained language models like BERT do not fully capture semantic information and often neglect the modeling of syntactic structures. This limitation hinders the model's ability to accurately interpret complex linguistic phenomena.  
In recent years, there has been a growing amount of content on social media that is represented jointly in multimodal forms, such as texts, images, and audio. Multimodal entity-relation extraction models have become an important emerging research direction in the field of natural language processing. [PERSON] et al. (2017) proposed a multimodal social relation extraction method based on few-shot learning, which uses limited data to learn and extract social relations from both texts and facial images. This marks the first attempt to incorporate both texts and images for social relation extraction. [PERSON] et al. (2018) introduced an entity-object and relation-image alignment pre-training task, which extracts self-supervised signals from large volumes of unlabeled image-caption pairs to pre-train the multimodal fusion module and enhance MERE performance. However, multimodal entity-relation extraction models typically require large amounts of labeled data across various types, and for traditional domains that rely solely on text data, the benefits of multimodal methods are not as apparent. As a result, the range of applicable scenarios is somewhat limited.