## 3 Methodology  
We used Google Scholar to search for articles related to NER and RE and specifically papers used in the context of clinical text. We also checked for publications where the above mentioned techniques are used in the radiation oncology domain. We discovered that there is very limited work on NER and RE in the radiation oncology domain; however, we did notice that there are a plethora of publications in using NER and RE in the clinical text in general. Figure 2 provides a high-level overview of the steps carried out to select research articles for the survey. For clinical NER, search terms such as 'Clinical Named Entity Recognition', 'NER in Radiation Oncology', 'Deep learning Clinical NER', and 'Machine Learning based clinical NER' were used. From the resulting articles, we categorized them based on the language used for NER i.e., English, Chinese, and Italian, among others. Next, we classified the articles based on the type of approach used for NER; we found that a majority of them used ML-based approaches, and only a few articles within the machine learning class used deep learning-based methods. Overall, for clinical NER, we selected around 23 papers, out of which 19 articles used machine learning-based approaches and 3 articles used rule-based methods while 1 article used a dictionary-based approach. Since 2018, most of the clinical NER models used only ML models, we discuss such methods in greater detail. Figure 3a shows a representation of various clinical NER models that were identified; we came across \(\sim\)8 papers that use ML approaches to develop NER models for clinical text. Figure 3b represents the distribution of ML-based clinical NER models. For clinical RE, we used the search term, 'Clinical Relationship Extraction' and obtained a number of research papers on clinical information extraction. After going through them, we found out that most people consider this to be a classification problem using machine learning models. Hence to filter out more of these papers, we again used the search term, 'Supervised Clinical Relationship Extraction'. Next, we used our judgement to use the search term, 'Unsupervised Clinical Relationship Extraction' to see if the community focuses on clinical RE without data-annotation. The last search term for clinical RE is 'Rule based Clinical Relationship Extraction' as we found out from the first search that rule-based methods are also used to some extent besides ML-based methods. From the top results of this search process, we manually identified the relevant papers based on their closeness to clinical RE and considering the diversity of the presented methods. We also kept the search results mostly limited to papers after 2016; however, this filter could not effectively find clinical RE-based articles using rule-based and unsupervised learning-based approaches. Not much work was conducted on clinical RE using unsupervised learning-based approaches because, in the clinical domain, most datasets are annotated and the supervised approaches are able to outperform these approaches in most cases, which are discussed later; we could only find two papers in this area. Rule-based methods have been used for clinical RE to some extent, but most of the noteworthy work was conducted before 2015-2016. After that, the application of supervised learning-based approaches for clinical RE started escalating distinctly and the focus on other approaches diminished. Hence, we manually identified two papers using rule-based approaches after 2016; both were published in 2021. We also manually chose three earlier papers using rule-based methods as they were popular in the past. We manually chose the 16 top, relevant, diverse papers using supervised learning-based approaches after 2016. We also considered another noteworthy supervised learning-based method for clinical RE before 2016. Out of these 17 articles, 15 papers used traditional ML and deep learning-based approaches and 4 papers used language models, with 2 papers using both language models and ML.