## 1 Introduction  
Owing to the rapid development of biomedical research, there is a large number of biomedical publications available online in an electronic format, and this number is increasing every year. For example, the number of articles in PubMed, a biomedical literature database, is increasing by approximately 1 million documents each year [1]. Biomedical reports, which contain valuable information on new discoveries and knowledge, have been continually added to the overwhelming amount of literature. Because medical literature publications contain a wealth of biomedical information, using publication data to solve a variety of biomedical problems, such as relationship extraction, has become a popular method in recent years [2; 3]. As a result, there is a high demand for automatic knowledge extraction from biomedical literature highly demanding. When processing large amounts of unlabeled unstructured data, such as treatises, word embeddings technology [4; 5] is an ideal approach for obtaining semantic relationships between words. Word embeddings were introduced to represent a single word as a low-dimensional vector that captures the frequencies of co-occurring adjacent words, and the mathematical similarity between the word vectors captures the similarity in meaning between the words. Compared to ontology-based approaches, such as the Unified Medical Language System (UMLS) and WordNet [6; 7; 8], word embedding technology has the following advantages: (1) it saves time and resources because it does not require human involvement; (2) it can analyze big data and produce results that humans are incapable of producing; (3) up-to-date results are available by feeding up-to-date corpora. The Word2 vec [4,5] model proposed by [PERSON] in 2013 is one of the most popular word embedding techniques because of its excellent term extraction performance [9]. The extracted terms form the basis of the application of biomedical natural language processing (NLP), such as medical information retrieval, relation extraction, and recommendation systems. More recently, deep masked language models pretrained with unsupervised learning have achieved state-of-the-art in a lot of NLP-related tasks. Bidirectional Encoder Representations from Transformers (BERTs) [10] were the first to apply the transformer architecture as a general framework for NLP. The pretrained model is available and can be fine-tuned for a different study. Some studies, like BioBERT [11], already pretrained such models based on BERTs using biomedical texts, fine-tuned them for downstream tasks, and performed better than most previous biomedical NLP models. Previous studies in the biomedical field in recent years have investigated how to improve the performance of the Word2 vec model in extracting similar biomedical terms. First, researchers studied the setting of model parameters [12, 13, 14]. Subsequently, studies were conducted from a data perspective. For example, a study by [PERSON] et al. [15] examined the corpus domain effects on measuring semantic relatedness and similarities between biomedical terms (i.e., clinical notes, PubMed Central (PMC) articles, and Wikipedia), and the results showed that the model trained on the PMC dataset was superior to that trained on clinical notes, and the performance of the model did not increase when the dataset reached a certain size (100 million words). [PERSON] [16] studied data size, recency, and section of publications, and their results for data size are consistent with those of [PERSON] [15]; they concluded that increasing the size of the dataset does not always enhance the performance. There is a certain point at which the performance reaches its peak, and as more data are added, more noise starts to affect the performance. These two studies demonstrate that large amounts of data cannot always guarantee effective word embedding in biomedical NLP. However, few studies have directly compared the effectiveness of word embedding in different resources (e.g., medical and general domains) [9].