## 1 Introduction
### Introduction to the NLP-Enhanced Clinical Trials Research  
Clinical trial texts involve valuable information for medical and clinical research, contributing to the solution of problems regarding the quality of healthcare and clinical decision support. However, usually, a significant portion of essential clinical trial information is documented and stored within unstructured texts, making it challenging to effectively and precisely extract useful information. Furthermore, it can be time-consuming and error-prone to convert such unstructured texts into structured ones, which may fail to capture multifarious information [3]. Thus, the extraction of appropriate features from clinical narratives consists of unlocking the hidden knowledge, as well as enabling the conduction of advanced reasoning tasks, for example, diagnosis explanation, disease progression modeling, and treatment effectiveness analytics [4]. There are at least two types of motivations worthy of being highlighted for the conversion of unstructured texts into structured ones [5], including reducing the time of manual screening and enhancing the reuse of such data to realize the automatic processing of large-scale texts. As the production of unstructured clinical trial data is rapid and on a large scale, it is of great need to mine such textual data and to further generate structured representations through automatic approaches by adopting NLP techniques. NLP concerns natural language human-machine interactions, particularly in terms of enabling machines to process and analyze large-scale natural language data. Applications of NLP have long been widely considered among scholars (e.g., [6; 7; 8]). As a computer-driven approach to facilitate human language processing [9], the adoption of the NLP techniques for clinical information processing has been popular for decades. There are several effective applications available in our daily life, for example, MedLEE [10]. However, clinical NLP, also referring to the use of NLP techniques that are developed and adopted for supporting healthcare through unlocking clinical information hidden within clinical narratives, is still an emerging technique [11]. Although medical documents in forms of plain texts contain essential information about patients, they are seldom available for structured queries. Besides the structured data, the provision of structured information extracted from natural language documents such as electronic health records (EHRs) [12; 13; 14] is expected to facilitate clinical trial research [15]. EHRs offer chances to improve the healthcare of patients, incorporate performance measurements in clinical practice, and assist clinical research [16]. EHR systems offer significant potentials for supporting quality improvement and clinical research. In addition, to fully take advantage of the electronic medical records (EMRs), scalable methods like NLP are needed to transform unstructured text to structured data that can be adopted for evaluation or analysis [17]. To that end, NLP has increasingly become a valuable and effective technique for processing clinical trial texts, most of which are unstructured and large-scale. Currently, several reviews on the NLP-enhanced clinical trial research and its relevant topics are available, as summarized in Table 1. For example, based on 43 studies concerning the NLP of clinical notes on chronic diseases, [PERSON] et al. (2019) [18] conducted a systematic review to explore the development and uptake of NLP approaches adopted to deal with textual clinical notes data on chronic diseases. The existing relevant reviews were conducted mainly by systematic reviews, suffering from the following disadvantages. First, as most studies used manual coding methods, the numbers of reviewed articles were relatively limited. Second, manual analyses might be error-prone as they involved a tedious and laborious coding process. In addition, the results of a small sample of the NLP-enhanced clinical trial studies could not be generalized to the whole community.