# Normalization of Web of Science Institution Names Based on Deep Learning
## 1 Introduction  
Academic evaluation is a process of assessing and measuring researchers, institutions, or disciplinary fields. Its purpose is to evaluate their contributions and impact in the academic community, as well as determine their reputation and status within specific disciplinary domains. It is an important factor in government decision-making and resource allocation. As the quantity and quality of publications serve as significant indicators for academic evaluation, Web of Science (WOS), one of the world's most renowned academic citation databases, is commonly used for academic research evaluation [1], ranking [2], and comparison [3] by researchers and academic institutions. In addition, the results of an academic evaluation affect the analysis of university education. For example, [PERSON] [4] analyzed 17 communication and journalism courses from eight of Europe's highest-ranked universities in the field of communication based on the QS World University Rankings to assess the university's educational program. However, according to a large-scale analysis conducted by [PERSON] [5], the lists provided in WOS's Essential Science Indicators (ESIs) are not as reliable and accurate as one might expect. Approximately 25% of author names (consisting of the initials of their first name and last name) are shared by at least two different individuals. When explicit data are not provided by authors or publishers, data aggregators such as WOS or Scopus find it challenging to provide accurate or statistically reliable data. This issue is commonly referred to as the name ambiguity problem and can be divided into two parts: the one person, multiple names problem (where one author entity is associated with multiple name variants in different publications) and the one name, multiple persons problem (where one author name corresponds to multiple different author entities). Institutional information serves as an identity marker for authors in the literature. Research has shown that the probability of homonyms in secondary institutions is very low [6, 7]. One approach to identifying homonymous author entities is by extracting primary and secondary institution namesfrom addresses using patterns, such as comma separators and \"university, department, laboratory\". However, research by [PERSON] [8] revealed that out of 84 universities in Iran, there are 1668 name variants in WOS, primarily stemming from abbreviations, spelling errors, spatial variations, syntactic arrangements, and vowel/consonant and vowel/consonant combinations, with spelling errors accounting for 34.57% of the variants. Confronted with a vast number of non-standardized institution entities, there are cases of the mislabeling and underlabeling of institution data in the institution lists of the ESIs and InCites (mislabeling refers to indexing an address belonging to institution A as institution B, while underlabeling occurs when an address belonging to an institution is not indexed under that institution). Due to reasons such as author spelling errors, transcription errors in systems, translation issues, variations in institution and department names, and the use of informal names or abbreviations, the same institution may have multiple different representations (Table 1), or an institution entity may be transcribed as another institution entity. Scholars have conducted extensive research on the institution name synonym recognition task (Table 2). In the early stages of research, scholars extensively explored the similarity of institution names from both character and word perspectives using methods, such as edit distance [9] and Jaccard similarity [10]. However, institutions with low literal similarity may refer to the same entity, such as \"Chinese Academy of Science\" and \"CAS\" (full name and abbreviation) or \"Chinese Academy of Science\" and \"Chinese Acad Sci\" (full name and keyword abbreviation). Conversely, institutions with high literal similarity may be distinct entities, for example, \"Fukushima Univ\" and \"Fukushima med Univ\".