# An Improved Model for Medical Forum Question Classification Based on CNN and BiLSTM
###### Abstract  
Question Classification (QC) is the fundamental task for Question Answering Systems (QASs) implementation, and is a vital task, as it helps in identifying the question category. It plays a big role in predicting the answer to a question while building a QAS. However, classifying medical questions is still a challenging task due to the complexity of medical terms. Many researchers have proposed different techniques to solve these problems, but some of these problems remain partially solved or unsolved. With the help of deep learning technology, various text-processing problems have become much easier to solve. In this paper, an improved deep learning-based model for Medical Forum Question Classification (MFQC) is proposed to classify medical questions. In the proposed model, feature representation is performed using Word2 Vec, which is a word embedding model. Additionally, the features are extracted from the word embedding layer based on Convolutional Neural Networks (CNNs). Finally, a Bidirectional Long Short Term Memory (BiLSTM) network is used to classify the extracted features. The BiLSTM model analyzes the target information of the representation and then outputs the question category via a SoftMax layer. Our model achieves state-of-the-art performance by effectively capturing semantic and syntactic features from the input questions. We evaluate the proposed CNN-BiLSTM model on two benchmark datasets and compare its performance with existing methods, demonstrating its superiority in accurately categorizing medical forum questions. medical forum question classification; bidirectional LSTM network; convolutional neural network; deep learning +
Footnote †: journal: Medical Forum Question  
in terms of categorizing questions [8]. However, these approaches have not completely addressed specific problems such as ambiguity in the question, lexical gap, and polysemy problem [9]. The QAS involves three main stages, namely question processing, document processing, and answer processing. QC is a subtask of question processing, which deals with identifying the question type posed by the user, based on possible categories of answers. The QC assists the QAS to eliminate categories of answers that are irrelevant to the questions. The types of questions also play a vital role in determining the types of answers proposed by the system. For instance, some questions start with \"who\", \"when\", \"what\", \"where\", \"how\", \"why\", etc. The questions that start with \"who\", \"where\", \"what\", and \"when\", are considered to be factoid questions and the expected answer type for these questions is short and simple. However, the questions that start with \"how\" and \"why\" are non-factoid questions and the expected answer type for these questions are long and complex. There are three main techniques used for the QC task, namely rule-based, machine learning, and hybrid techniques [7; 10]. Rule-based techniques use a set of rules programmed to generate pre-defined answers. Machine-learning techniques can learn from data and classify questions efficiently. Machine-learning techniques, such as Neural Networks (NN), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN) have been used for question classification. Hybrid techniques combine both rule-based and machine-learning techniques to classify questions. Deep learning techniques were derived from artificial neural networks and today they have become a principal area of machine learning [2; 11]. It involves various techniques, such as Convolutional Neural Network (CNN) [12], Long Short Term Memory (LSTM) which derives from a traditional Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), etc. [13; 14]. The medical QAS differs from the open-domain QAS [15]. In the medical domain, there are domain-specific terminologies and domain-specific question types, which differ for domain expert users and non-domain expert users.