## 2 Related Work
The MECT combines Chinese character features with radical-level embedding in a two-stream system The authors in [4] proposed a named entity recognition method that combines pinyin and POS features to address the phenomenon of multiple pronunciations of one character and multiple characters of one pronunciation in Chinese medical records. The model performance was improved by effectively integrating the glyph, POS, and pinyin features through the scaled dot-product attention module. Studies have shown that the introduction of prior features in the Chinese named entity recognition task has a positive effect on the performance improvement of existing models. Named entity recognition, as an important part of the joint extraction task, provides a new idea for the joint extraction model when processing Chinese text. [PERSON] et al. [18] proposed the AMFRel joint extraction model, which generates an encoding vector containing POS information by extracting electronic medical record text and POS features. After subject extraction, the model uses the information fusion module to enhance the text structure features and extracts the object according to specific relations, finally realizing the extraction of medical text triples. [PERSON] et al. [19] utilized the SpERT framework, combining word embedding with POS features. The feature fusion strategy was used to solve the redundancy problem in the entity extraction process based on the span method, while improving the accuracy of joint extraction. [PERSON] et al. [20] combined word vectors with character vectors, incorporated position information to ensure the correct word order, and used a hierarchical labeling strategy to effectively mark multiple relations and object entities based on the main entity information, thereby achieving an effective extraction of overlapping relations. [PERSON] et al. [21] proposed a relation extraction model that integrates Chinese pinyin, character, and word features. By incorporating entity distance, sentence length, and part-of-speech information, the model markedly improved the performance of triple extraction from unstructured text. [PERSON] et al. [22] employed ON-LSTM to introduce textual information, combining it with part-of-speech and glyph features to enhance the complementarity of similar word vectors. The experimental results demonstrated that the fusion of these features significantly boosted the model's capability to extract triples from Chinese legal texts. Based on the above analysis, this paper considers introducing POS features and pinyin features in the joint extraction task and designs a corresponding feature fusion mechanism to integrate it into the model training process, thereby improving the model's text representation ability and enhancing model performance. Contrastive learning is a machine learning technique that enhances the similarity between similar samples and expands the difference between different samples [23], which helps to improve the joint extraction performance of the model. Its core concept is to organize the embedding representation of samples in the latent space to ensure that the embeddings of the same or semantically related samples (positive sample pairs) are closely adjacent, while the embeddings of different or unrelated samples (negative sample pairs) are as far apart as possible. In recent years, contrastive learning has shown good per formance in natural language processing tasks. [PERSON] et al. [24] first introduced the contrastive learning framework into the field of natural language processing, using co-occurring words as the basis for measuring semantic similarity and effectively learning the embedding representation of words through negative sampling technology. This approach improves the representation quality of words and phrases in high-dimensional space, enabling them to more accurately reflect their semantic features. Subsequently, [PERSON] et al. [25] proposed the CERT model, a pre-trained language representation model based on contrastive learning.