## 1 Introduction
[PERSON] [18] proposed a neighborhood-oriented packing strategy to pack spans with the same starting lexical elements into a training example in order to better distinguish the entity boundaries and extract the relationships through strategic packing. Through leveraging the pipelined model of span representation, state-of-the-art performance can be attained through fine-tuning BERT. The relationship extraction task associates different entities and recognizes the type of relationship between them, which can be abstractly represented as edges and nodes in the graph theory for the relationships and entities. Semantic dependency directly links dependent arcs of linguistic units through immediate semantic connections and annotates them with relevant semantic relationships. Semantic dependency focuses on the semantic-factual or logical relations between real words and is able to express deeper semantic information [19]. [PERSON] [20] proposed an approach which consisted of incorporating the glyph information of Chinese characters to enhance the model's ability to deeply characterize the text in named entity recognition for power equipment maintenance records. [PERSON] [21] proposed the semantic enhancement of words with multiple meanings and similar glyphs through incorporating pinyin and glyph information. [PERSON] [22] proposed a typed Tree-LSTM model that embedded sentence meanings into dense vectors using sentence dependency parsing structures and dependency types. Relationship extraction and named entity recognition are similar tasks that belong to the same natural language processing field. Based on the idea of feature fusion and the characteristics of relationship extraction, this paper combined the semantic dependency information and the lexical embedding information with BERT. It aimed to improve the entity association and semantic characterization capacities of the BERT model. Compared with the English language, the most obvious feature of the Chinese language is the ambiguity of word boundaries and the absence of separators to represent word boundaries [23; 24]. In English, there are separators between the words that identify the boundaries with each word having a distinct meaning, which is not the case in Chinese. Therefore, the relational extraction in Chinese text requires its segmentation. However, there is no established lexicon accessible for the segmenter to employ in the power dispatching domain. Employing a general-domain segmenter within the power grid field leads to considerable inaccuracies. Therefore, word-based encoding is used. The cross-entropy loss function is a common loss function used to measure the gap between the model output and the actual label in classification problems. It is widely used in various models, such as classification models in machine learning and neural network models in deep learning. In the classification tasks, the cross-entropy loss function is employed to assess the dissimilarity between the probability distribution generated via the model's output and the actual distribution of labels. During the training process, the model will continuously adjust the parameters using the gradient descent algorithm to make the overall loss function as small as possible. Through minimizing the cross-entropy loss function, the model can more accurately predict the class labels of each sample in the classification problem, which improves its performance. The main contributions of this paper are summarized as follows:  
1. Lexical and semantic dependency dictionaries were constructed, and the RoBERTa word embedding layer was embedded by effectively fusing lexical and semantic dependency information. This made the model learn more dependencies allowing for the extraction of the relationships between the different entities, measuring the model loss through the cross-entropy loss function, and optimizing its parameters through back-propagation;
2.