# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?
### Convolutional Neural Network (CNN)  
In order to answer this research question, approx. 42 research studies were reviewed to find various deep learning techniques that are used in short-text semantic similarity \begin{table}
\begin{tabular}{c c} \hline \hline
**Research Questions (RQ)** & **Number of Studies** \\ \hline DL-based techniques in short-text semantic similarity (STSS) & 42 \\ Contextual based keyword selection approaches & 76 \\ Datasets for short text similarity & 34 \\ Challenges or limitations of different methods in STSS & 25 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of studies per research question. Figure 6: Number of studies from the year 2017 to 2023. (STSS) and question-answering systems (QAS). Deep learning is becoming more and more prevalent in the field of natural language processing (NLP). Several models of attention-based neural networks sparked the interest of many academics, such as the convolutional neural network (CNN), the recurrent neural network (RNN), and the bi-directional encoder representation from the transformer (BERT) [1]. Table 7 presents a summary of the convolutional network (CNN) model in various categories of short text, to which [PERSON] et al. [1] contributed their research in the field of short-text semantic similarity (STSS). They used a convolutional neural network (CNN) for short text classification to find the related words, and, further, they used external knowledge to understand the conceptual meaning of answers and words from a short text. However, they also used Jaro-Winkler similarity to find the grammatical errors in sentences. [PERSON] et al. [16], developed short answer grading systems by using a convolutional neural network (CNN). The available model worked as a text classifier to understand the Chinese language from students' answers. However, they also used binary classification to grade the answers as correct or incorrect. Moreover, [PERSON] et al. [17] presented the dual embedding convolutional neural network (DE-CNN) to understand the underlying meaning of the short answer. They used two embedding layers to understand the relevant context of sentence features and also utilized an attention embedding layer to get the concept representations from answers. [PERSON] et al. [18], proposed the CNN model to identify irrelevant answers from web-based question-answering systems (QAS). The length of answers was short, and the category of questions was a factoid. Moreover, the model proposed by that research could not answer the whole set of factoid questions. [PERSON] et al. [19] developed a character-level convolutional neural network (CNN) to understand short answers. Without any prior understanding of language and semantic structures, the model learned to anticipate the target information from answers. Additionally, the study ran into a number of difficulties when trying to score short answers, due to a lack of tools and generic strategies. In order to understand the short text semantically, [PERSON] et al. [20] developed a brand new semantic hierarchical paradigm for categorizing short texts. However, they further detected multi-scale SUs in short texts and introduced new information using pre-trained word-level embeddings. Furthermore, the emerging trends of big data provide a massive amount of short text. Earlier, the data was accumulated manually from power sources. However, [PERSON] et al. [21] proposed a convolutional neural network (CNN) and LDA to mine and understand the global features from the short text automatically. Moreover, many traditional algorithms of machine learning affect the generalization ability of short text. Due to this fact, [PERSON] et al. [22] presented a CNN with a support vector machine convolutional neural network (SVMCNN) to improve short text classification. Meanwhile, the researchers trained their model on TensorFlow by utilizing the Twitter social platform.