# MARIE: A Context-Aware Term Mapping with String Matching and Embedding Vectors
## 1 Introduction  
Due to the growing interest in text mining and natural language processing (NLP) in the biomedical field [1; 2; 3], data pre-processing is becoming an increasingly crucial issue for many biomedical practitioners. As datasets used in research and practice are often different, it is challenging to directly apply recent advancements in biomedical NLP to existing IT systems without effective data pre-processing. One of the key issues addressed during pre-processing is concept normalization, which refers to the task of aligning different text datasets or corpora into a common standard. Previous works on concept normalization have relied on either extracting meaningful features from standardized ontologies [4; 5; 6] or training machine learning models [7; 8]. Recently, deep learning models [9; 10; 11] have further improved normalization accuracy. However, these previous works are limited to a supervised learning setting, in which correctly aligned and standardized training datasets are required. If these manually aligned training datasets are not readily available, the normalization accuracy of these supervised approaches cannot be guaranteed. Without any additional annotated data, previously trained and supervised models cannot be fine-tuned for new datasets that exhibit completely different properties from their annotated counterparts. When the models fail to capture changes in the data distribution, their modeling capabilities cannot be transferred to the new datasets [12; 13; 14]. Therefore, these previous supervised learning models that were trained from standardized datasets cannot be easily applied in practice, in which mapping between non-standardized datasets is prevalent. However, creating new annotated or standardized datasets is extremely challenging. Due to geographical, organizational or operational factors, biomedical organizations have their own unique sets of standards and conventions for processing their text data. For example, medical institutions in the United Kingdom and the United States use a systematized nomenclature of medicine, using clinical terms (SNOMED CT) [15] to standardize their clinical data. In South Korea, on the other hand, medical institutions rely on completely different sets of clinical terminologies issued by the Korean Health Insurance Review & Assessment Service, due to its fee-for-service model. Therefore, creating a highly standardized annotated dataset for every mapping need is simply too costly. Despite its practical importance, unsupervised biomedical term mapping has not been actively explored. Previously, unsupervised learning-based mapping methods have relied on string matching-based approaches [16; 17]. Without any model training, they utilize the structural information of the terms to define a similarity score, thereby providing greater applicability in mapping non-standardized or small datasets. However, a similarity measure between terms is often defined beyond their structural similarity. Using word embedding methods [18; 19], we can also define a similarity measure between the terms based on their contextual similarity. As shown in the tasks of text classification [20; 21], sentiment analysis [22; 23] and machine translation [24], the contextual information captured by the embedding methods provides finer granularity to defining term similarities. In this paper, we introduce MARIE (Python implementation of MARIE is available online at: [[https://github.com/hank110/MARIE](https://github.com/hank110/MARIE)]([https://github.com/hank110/MARIE](https://github.com/hank110/MARIE))), a context-aware mapping method with string matching and embedding vectors. It is a new, unsupervised concept normalization method that maps biomedical terms from different datasets, based on both their structural and contextual similarities. Its approach for capturing structural similarity is identical to previous string matching methods.