## 4 Discussion
### Comparison with Prior Work  
Prior to this study, several researchers studied the effect of the corpus domain on the similarities between biomedical terms extracted by Word2 vec.  
[PERSON] et al. [21] used health-related Wikipedia articles and general Wikipedia articles to train multiple word embeddings to evaluate performance in analogical and health-related relationship search tasks and compare word embedding performance. They evaluated whether the results depended on the domain of the text corpus. The results showed that word embeddings trained on health-related Wikipedia articles performed better on health-related relationship search tasks than those trained on general Wikipedia articles. This is consistent with the results of the present study. The results of this study show that models trained solely on biomedical data performed better in extracting biomedical terms than models trained solely on general domain data (five pairs cannot be recognized by the models with a ratio of 10:0).  
[PERSON] [15] also investigated the corpus domain effects on semantic similarity and relatedness between biomedical terms (e.g., clinical notes, PMC articles, and Wikipedia). The 2010-2014 clinical notes obtained from the Fairview Health System were used. The PMC and Wikipedia datasets both contain all the data available as of September 2015. Modified versions of UMNSRS-Rel and UMNSRS-Sim were used as reference standards. The results showed that the model trained on the PMC dataset outperformed the one trained on the clinical notes (i.e., 0.62 vs. 0.60 for similarity and 0.58 vs. 0.57 for relatedness). This demonstrated the value of publication data for measuring semantic similarity and relatedness between biomedical terms. This is one of the reasons why the biomedical data used in this study were PMC data. This is significant, because access to clinical data is highly restricted, and difficult, to protect the confidentiality and security of patient health information. The PMC's open access biomedical articles have no such restrictions and are freely and easily accessible to all. In addition, PMC has many different research topics pertaining to new biomedical knowledge. Another important finding in the work of [PERSON] is that increasing the size of the corpus beyond a certain size (66 million tokens) would not enhance the performance of the model.  
The same result was found in a study by [PERSON] [16], where there was a certain point at which the performance reached its peak and as more data were added, more noise started to affect the performance. The reason was thought to be that as more data were added, the number of vocabularies increased, and more noise was introduced.  
Additionally, according to the results of [PERSON] [17], the combined medical and non-medical data achieved the best performance (i.e., the results of Wiki + PubMed + PMC data were better than those of PubMed + PMC data). The reason was thought to be that the Wiki-PubMed-PMC dataset was larger than the PubMed-PMC dataset; however, as a limitation, no further investigation has been conducted in this regard. In fact, in the study by [PERSON] [17], the sizes of these two datasets exceeded the specific size reported in [16], at which the model's performance reached its highest level. In other words, the reason why the combined biomedical and non-biomedical data reached the peak performance is not the increase in data size. According to the results of this study, the addition of a certain proportion of general domain data leads to the results reported by [PERSON] [17]. Some of these previous studies investigated (or did not investigate) the effect of the domain of the corpus on the semantic similarity between biomedical terms; however, the main difference from this study is that it is the first to compare the effectiveness of word embeddings from the perspective of the effect of the ratio of biomedical domain data.