# FA-RCNet: A Fused Feature Attention Network for Relationship Classification
## 1 Introduction  
Relation classification [1, 2] is an important task in natural language processing. In natural language processing task architectures, relational classification is an important antecedent between tasks, such as document summarization [3], question-and-answer systems [4], machine translation [5], and knowledge graphs [6]. The relationship classification task is a supervised task. The main objective is to extract relation information from a sentence with an entity identifier, determine the relationship between two entities in a sentence, and classify the relationship. Take the sentence in Table 1 as an example, the sentence includes \(<\)\(e_{1}\)\(>\), \(<\)\(e_{1}\)\(>\), \(<\)\(e_{2}\)\(>\), \(<\)\(e_{2}\)\(>\), which are four position markers. Tags are used to mark the exact location of entities in a sentence. The example sentence is labeled with two entities \"elephant\" and \"animal\". The task of relationship classification is to identify the relationship between the two entities as entity-origin(\(e_{1}\), \(e_{2}\)). Earlier traditional methods of relational classification used kernel-based approaches [7, 8, 9]. Kernel-based algorithms mainly include support vector machine (SVM), radial basis func \begin{table}
\begin{tabular}{c c c} \hline Sentence: “The \(<\)\(e_{1}\)\(>\) elephant \(<\)/\(e_{1}\)\(>\) descended from an aquatic \(<\)\(e_{2}\)\(>\) animal \(<\)/\(e_{2}\)\(>\).” \\ \hline \(E_{1}\): elephant & \(E_{2}\): animal & Relation: Entity-Origin(\(e_{1}\), \(e_{2}\)) \\ \hline \end{tabular}
\end{table} 
Table 1: Sample sentences. tion (RBF), and linear discriminate analysis (LDA). SVM is mainly used in relation classification tasks to classify entity relations. SVM is a classification model that solves binary classification problems through supervised learning. Such methods are of course helpful to improve the accuracy of relational classification tasks. However, SVM is based on the binary classification of the relationship based on the kernel function. Not all features can be divided according to the kernel function. When the relationship between entities is complex or there are multi-category relationships between entities, the error probability will increase. Therefore, the kernel-based relationship classification method has problems, such as a high error rate, low classification accuracy, and serious feature loss. Relational classification methods based on neural networks and deep learning have become a hot issue for research in recent years [10]. Such methods require only simple pre-work or even methods without pre-work for automatic learning of feature parameters, such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory network (LSTM), and neural networks based on attention mechanisms. In recent years, research has mainly been based on CNN and RNN-based relationship classification models. Such models typically use the traditional form of mapping literals to vectors. After the pre-training model was published, the focus of research gradually shifted to the pre-training model. The latest models basically use the feature output by the pre-trained model for downstream tasks. However, when using the pre-training model, the feature output of the last layer is habitually used for tasks, resulting in the importance of shallow features being ignored. The above model surpasses the traditional model in terms of relational classification effect but still has the following problems. First, the shallow features are ignored. The existing models [11; 12; 13; 14] retain only the deep features extracted by the neural network. Deep features have rich semantic information and are more suitable for performing relational classification tasks. However, shallow-level features have richer fine-grained features and clearer location information [15]. The shallow information can be used as auxiliary information in the classification network to enhance the effective part of the deep features.