# 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)?
### Recurrent Neural Network (RNN)  
With the emergence of short text semantic similarity, the recurrent neural network (RNN), along with various other models, give tremendous performance on STSS. [PERSON] et al. [23] proposed the recurrent neural network (RNN) with the convolutional neural network (CNN). The combined approach performed the semantic matching between various words; however, the model further used semantic representations between sentences to find the similarity. The study further achieved an F1 score of 0.751, and the precision rate was 0.760. The most common limitation that occurs while utilizing the recurrent neural network for short text semantic similarity is the gradient vanishing problem. In order to overcome the problem, [PERSON] et al. [24] presented the model known as long short-term memory (LSTM), which is often known as the variant of RNN. The model employed cosine similarity to calculate the distance and similarity between two short texts and also used backward propagation after the normalization process. Moreover, [PERSON] et al. [25] applied the RNN and CNN to the semantic features of the gender classification, and they included short text. In order to do this, they combined the semantic features for the classification process. [PERSON] et al. [26] presented multiscale CNN-RNN to represent the short text. The model was able to produce the word as level as character-level representations. Moreover, [PERSON] et al. [27] proposed the gated recurrent neural network (ABRNN) that automatically filters short tweets. The model extracted only those tweets that were relevant to a syndrome, such as asthma/difficulty breathing. Furthermore, [PERSON] et al. [28] presented various methods and limitations for short text similarity. They researched that recurrent neural networks (RNN) can utilize Tf-IDf vectors to better understand the similarity among short sentences. [PERSON] et al. [29] generated vector representations of short text through a recurrent neural network, and, later, they evaluated the model on a classification dataset known as DSTC, which is abbreviated from the dialogue state tracking challenge. Table 8 further illustrates the summary of RNN-based studies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Model** & **Purpose** & **Category** & **Feature Set** & **Experimentation** \\ \hline [PERSON] et al. (2020) [18] & Convolutional neural network (CNN) & \begin{tabular}{c} To identify irrelevant answers from a web-based community \\ \end{tabular} & Short answers & \begin{tabular}{c} Web-based forum \\ (Bing Query longs, WikiQA) \\ \end{tabular} & \begin{tabular}{c} Scikit learn library \\ Regression R-CNN \\ \end{tabular} \\ \hline [PERSON] et al. (2019) [19] & Character-level convolutional neural network (CNN) & \begin{tabular}{c} The model learns to anticipate the target information for short answers. \\ \end{tabular} & Short answers & \begin{tabular}{c} Short answer scoring by Hewlett \\ foundation \\ \end{tabular} & \begin{tabular}{c} NVIDIA 940 mx \\ GPU \\ Max pooling, \\ min pooling \\ \end{tabular} \\ \hline [PERSON] et al. (2017) [20] & Semantic clustering and convolutional neural network (CNN) & \begin{tabular}{c} Developed a brand \\ new semantic \\ hierarchical \\ paradigm for \\ categorizing short \\ texts. \\ \end{tabular} & Short text categorization & Google snippets & \begin{tabular}{c} K-max pooling \\ SVM parser \\ \end{tabular} \\ \hline [PERSON] et al. (2022) [21] & Convolutional neural networks (CNN) & \begin{tabular}{c} To mine and understand the global features \\ (CNN) \\ \end{tabular} & Short text classification & \begin{tabular}{c} 1000 defect text \\ data from a power \\ campaign in a \\ northwestern \\ \end{tabular} & \
\begin{tabular}{c} LDAvis toolkit \\ Confusion matrix \\ Python 3.7 \\ \end{tabular} \\ \hline [PERSON] et al. (2018) [22] & CNN+SVM &
\begin{tabular}{c} To understand \\ short text \\ information \\ \end{tabular} & Short text classification & Twitter & TensorFlow \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cont.