# A Review on Medical Textual Question Answering Systems Based on Deep Learning Approaches
## 2 Medical Question Answering Systems
### Tasks of Medical QAS
#### 2.1.1 Question Processing  
The point of focus in this module is to identify the question word. QAS accepts user input (a question in natural language) to evaluate and classify it. The evaluation is carried out to discover the type of question or what the question is, focusing on avoiding uncertainties in the answer [35]. Question-processing converts a question into a search query. Then, stop words (words that are filtered out before or after processing natural language data) and words with a particular part of speech are removed. By using deep learning technology, there is the possibility of creating a vector that can convey the exact meaning of a sentence. Classifying a question is an important step in a QAS' process.  
[PERSON], et al. [37] highlighted that the main procedure is to translate the semantic relations expressed in questions into a machine-readable representation to deeply and efficiently analyze natural language questions. [PERSON], et al. [38] presented two main approaches to question classification, namely, manual and automatic methods, which is the first time that handmade rules have been used to identify the expected answer types. Although these rules can provide accurate results, they have some drawbacks such as being time-consuming, monotonic and not scalable.  
[PERSON], et al. [39] classified the question type as How, Where, What, Who, Why questions, etc. This type of definition facilitates better answer detection. In contrast, automatic classification can be extended to new question types with acceptable accuracy [40]. Question processing can be divided into two main procedures [41]: the structure of the user's question needs to be analyzed first, then the question needs to be transformed into a significant question formula, which is compatible with QA's domain.  
Questions can also be described by the type of answer we expect to obtain. The question types include general questions with Yes/No answers, factoid questions, definition questions, list questions and complex questions [42]. General questions with Yes/No answers are the ones whose expected answer is one of two choices, one that affirms the question, and another that denies the question [43]. Factoid questions are determined by a question asking about a simple fact and receiving a concise answer in return [44]. Typically, a factoid question starts with a Wh-interrogated word, such as When, What, Where, Which, and Who [42], for example, \"Which is the most common disease attributed to malfunction of cilia?\" Definition questions get in return a short passage [36]: \"What is diabetes?\" A list question needs a set of entities that fulfils the given criteria [44]: \"What are the symptoms of skin cancer?\" Complex questions deal with information in a given context, whereby the answer is a combination of retrieved passages, for example: \"What is the mechanism of action of a vaccine?\" To implement this combination, different algorithms are used, such as Round-Robin, Normalized Raw-Scoring and Logistic Regression [45].  
Figure 2: General architecture of QAS.  
Questions asked by users may be in any form. It is the role of the system to deal with all types of answerable question [40]. We cannot imagine any format for the question, the question word may be placed at any place in the question, and it cannot cause any problem. The variation in question words is the main issue on which researchers need to focus.