# Normalization of Web of Science Institution Names Based on Deep Learning
## 1 Introduction
Utilizing Multiple Contextual Information of Institution Entities: The current research often relies on single-context matching, where only the most similar context containing the institution entity is considered during the institution matching process. This \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{String Similarity-based} & Simple and easy to implement effective in handling spelling errors or minor variations. & Less effective in handling semantically similar but structurally different names or high literal similarity of distinct institution names. \\  & Can adapt to various fields and languages of bibliographic data because they rely on contextual information and statistical features. & Depend on the selection and construction of statistical features, requiring substantial data support, and may be influenced by data quality. \\ \hline \multirow{4}{*}{Rule-based} & Simple and intuitive; effective in handling obvious cases. & Rule formulation can be complex and require human involvement; less effective in handling complex cases. \\ \hline \multirow{4}{*}{Entity Linking-based} & Can leverage rich information in knowledge bases; effective in handling complex cases; enables automated construction of institution standard files. & Require high-quality knowledge base support; less effective in handling new institutions not present in the knowledge base. \\ \hline \multirow{4}{*}{Deep Learning-based} & Can automatically learn and extract features and perform feature combinations; effective in handling complex cases. & Require a significant amount of annotated data; training and fine-tuning the models can be complex. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Methods for institution name synonym recognition. approach fails to fully leverage the multiple contextual information that an institution may appear in, thereby limiting the recognition accuracy. To address these issues, this study proposes a synonym relationship recognition model that integrates multi-granularity features and multiple contextual information. The model combines Char-CNN and Word2 Vec techniques to extract text and semantic features of institution entities and efficiently fuses different features using a Highway network. The model also utilizes BiLSTM combined with a multi-context matching layer to integrate the performance of institution entities in different texts, resulting in a comprehensive entity representation. Finally, the model uses cosine similarity to calculate the similarity between institutions, enabling accurate synonym relationship recognition. This multidimensional feature fusion approach effectively improves recognition accuracy and is suitable for handling complex institution name variants and structures. This paper's contributions can be summarized as follows:  
* Addressing the deficiencies in feature extraction and fusion for institution name standardization: This paper proposes the construction of an embedding layer that extracts and fuses two types of features. There may exist correlations and dependencies between different feature categories. By extracting features from different categories within a unified model, the model can share learned knowledge and representations, thereby improving generalization and effectiveness. * Solving the issue of underutilizing multiple contextual information of institution entities: This paper introduces a method based on bidirectional matching and multi-context fusion. This approach effectively leverages the multiple contexts in which institution entities may appear. By considering and integrating information from different contexts, the model achieves a more comprehensive understanding of institution entities, leading to improved accuracy in recognition. These contributions aim to enhance the performance and robustness of the institution name standardization task by improving feature extraction, fusion, and the utilization of contextual information.