# Normalization of Web of Science Institution Names Based on Deep Learning
## 3 Institutional Synonym Recognition Model  
### Overview of the Proposed Model  
[PERSON] [25] conducted research indicating that the use of subword features, such as stems and affixes, can effectively identify the abbreviated forms of words. This approach reveals the potential of subword features in capturing the microstructure of language, particularly in the recognition of abbreviations and contractions, where it demonstrates high performance. Based on this finding, we have chosen to employ Char-CNN to extract character-level features from institution names. Char-CNN allows for the in-depth analysis of the internal structure of words, enabling the identification and learning of specific character sequences or combinations. This capability proves particularly effective in handling spelling errors, abbreviations, and domain-specific language. By incorporating Char-CNN in our approach, we not only enhance the model's ability to perceive subtle textual differences but also improve its robustness when dealing with anomalous text.  
Word2 Vec is capable of capturing the semantic similarity between words, but it does not capture the importance and distribution of words within a document collection. Therefore, we utilize Word2 Vec in combination with TF-IDF to obtain word-level features. In this paper, we employ Highway networks [26] to integrate character-level and word-level features. This network structure effectively controls the flow of information between different features through its gating mechanism.  
Specifically, the sigmoid function in Highway networks determines the proportion of information flow between character-level and word-level embeddings, while the fully connected layer appropriately transforms and adjusts the passed information. This approach allows for the model to flexibly integrate text features at both the character and word levels, fully leveraging the fine-grained information from character-level features and the semantic richness of word-level features. As a result, it enhances the accuracy and robustness of institution name recognition and matching.  
In the task of institution synonym recognition, understanding and utilizing the hierarchical relationships of institutions are crucial for accurately determining whether two institutions refer to the same entity. To address this, we employ BiLSTM (Bidirectional Long Short-Term Memory) to aggregate contextual semantic information. BiLSTM is effective in capturing both preceding and succeeding contextual details, including semantic and syntactic information, thus providing a comprehensive semantic understanding.  
Furthermore, considering the ambiguity and fuzziness inherent in natural language processing, our model incorporates multi-context matching techniques. By analyzing and comparing the relationships between different contexts, the model enhances its ability to capture semantic information. Multi-context matching allows for the model to automatically determine which contexts are more critical for interpreting the semantics in a sentence by learning the matching relationships and corresponding weights between different contexts. This approach not only improves the model's expressive power but also enhances its robustness and accuracy when dealing with semantic complexity.  
Ultimately, by calculating the cosine similarity of the fused feature vectors, the model is able to determine whether two institution entities are synonymous. The architecture of the model is illustrated in Figure 1.