# NAS-CRE: Neural Architecture Search for Context-Based Relation Extraction  
[PERSON]  
1 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; [EMAIL_ADDRESS] (R.Y. ); [EMAIL_ADDRESS] (D.L.) [PERSON]  
1 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; [EMAIL_ADDRESS] (R.Y. ); [EMAIL_ADDRESS] (D.L.) [PERSON]  
3 CSC Systems Engineering Research Institute, Beijing 100094, China; [EMAIL_ADDRESS] 34 CSRC Intelligent Innovation Research Institute, Beijing 100094, China 45 School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China; [EMAIL_ADDRESS] 46 Zhongguancum Laboratory, Beijing 100094, China; [EMAIL_ADDRESS] 6  
[PERSON]  
5 School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; [EMAIL_ADDRESS] (R.Y. ); [EMAIL_ADDRESS] (D.L.) [PERSON]  
6 Zhongguancum Laboratory, Beijing 100094, China; [EMAIL_ADDRESS] 6C8 SC Intelligent Innovation Research Institute, Beijing 100094, China 6C8 SC Intelligent Innovation Research Institute,  
sentence on entity extraction and demonstrate the effectiveness of their proposed method in the task of single-sentence relationship extraction. To simplify the expression, this paper defines the method as context-based relation extraction (CRE). Unlike traditional methods that focus solely on the entities themselves, the CRE method expands the scope to the entire sentence, allowing for a more comprehensive capture of the relationships between the target entities. Specifically, as shown in Figure 1, there exists a sentence containing two entity pairs (\(e_{1}\), \(e_{2}\)) and (\(e_{3}\), \(e_{4}\)). When extracting the relationship \(r_{1}\) between \(e_{1}\) and \(e_{2}\), the influence of the relationship \(r_{2}\) between the entity pair (\(e_{3}\), \(e_{4}\)) is also considered. The focus of the aforementioned work has concentrated on innovations in model architecture and enhancements in performance, often neglecting the critical importance of internal parameter tuning, a process that typically relies on manual model design. This manual adjustment [11] not only consumes a significant amount of time and labor resources but is also susceptible to human factors, particularly the subjective biases and constraints of researchers' experiences. Taking sentence feature extraction as an example, although LSTM networks [12; 13; 14], as an advanced variant of RNNs, have been applied, parameter tuning still requires manual intervention. This demands considerable time investment and a deep level of expertise. Even with careful adjustments, the structure of LSTM networks may not reach an optimal configuration and often lacks adaptability to changes in data. Considering the complexity of deep learning in relationship extraction network models and the breadth of parameters involved, designing an efficient model is undoubtedly a challenging task. To fully explore and leverage feature information, researchers must meticulously adjust the network structure and parameters to achieve optimal performance. Therefore, the main research challenge in executing context-based relationship extraction tasks is how to implement automated searches for model structures and optimal configurations to efficiently complete the extraction tasks. In response to the aforementioned challenges, this paper proposes a mechanism for the dynamic adjustment of the internal structure of neural networks, aiming to accurately capture relational expressions within sentences. This study adopts an encoder capable of precisely reflecting the semantic features of entire sentences. The proposed method is termed neural architecture search for context-based relation extraction (NAS-CRE), which seeks to effectively address the challenge of dynamic optimization of network structures. This work is inspired by the current wave of interest in neural architecture search (NAS).