# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 6 What Are the Available Datasets Used for Short-text Semantic Similarity? To answer this question, we reviewed approx. 34 studies for the selection of suitable datasets specific to short-text semantic similarity. Movie Review (MR): The MR dataset is an open-source dataset, which generates positive and negative movie reviews. This dataset contains 10,662 pairs. The average length of each sentence is 20 words [16]. Due to this account, [PERSON] et al. [113] used the MR dataset for sentiment analysis. They further imported the NLTK features in Python and focused them on the built-in classifier to compare the accuracy with other datasets. The dataset is suitable for detecting user reviews, recommender systems, text classification, and  
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sentiment analysis. Many researchers applied a number of algorithms to this dataset and achieved maximum accuracy rates [114; 115; 116; 117; 118]. TREC: The TREC dataset is used for question-answering systems (QAS). The total size of QA pairs is 6452 [111; 119]. The dataset is further partitioned for the training and testing phases. For the training phase, it includes 5952 QA pairs, and, for testing, the dataset includes 500 test items [120]. Furthermore, it also includes six categories for the selection of answers. However, the responses can be positive or negative. TRECQA raw and TRECQA clean are the recent versions of the dataset; the average length of each sentence is 20 words. As for the selection of correct answers, researchers imported the dataset by using Python 3.6 with the PyTorch library and utilized the Colab platform [1; 121]. AG news: This dataset is an English news article dataset [112; 122; 123] that includes a 127,600 data size. The dataset is additionally split into a training phase and a testing phase. The testing phase includes 7600 data sizes, whereas the training phase includes 120,000 [16]. The length of the sentence is seven words, which is suitable for short text. However, the dataset is divided into three categories for experimentation. The dataset is suitable for data mining and information retrieval techniques. However, [PERSON] et al. [122] used the NLTK library for English stop-words removal, and, for the dataset cleaning and preprocessing, they employed the WordNet lemmatizer. Mohler dataset: [PERSON] et al. [40] created a dataset that has been widely used for automated short-answer grading systems (ASAGs). The dataset is the domain-specific dataset. They collected the data from an introductory computer science class at the University of North Texas. From ten assignments, two exams were conducted. The dataset, however, includes 2273 student responses to the 80 questions. Meanwhile, most of the questions are factoids and descriptive. The average length of reference answer is 15-20 words. The student answers were scored by two teachers manually on a scale of 0-5. The dataset has two categories for grading purposes: (i) correct and (ii) incorrect. Moreover, to import the dataset, many studies used Python 3.6 with the NLTK library. The Mohler dataset is the benchmark for grading short answers and is widely evaluated by many studies [36; 37; 38; 85; 124]. However, the Adam optimizer, different regression techniques, and models have been used to grade the student answers. SST-2: The SST-2 is the Stanford Sentiment Treebank (SST) dataset, which is one of the most popular movie review datasets [125]. The SST-2 is the recent extension of the movie review dataset. Constituency parsers and tree structures are suitable for the SST-2. Moreover, the SST-2 is a collection of 9613 sentence pairs. The average length of each sentence is 19 words. The dataset is divided into five categories: very negative (labeled 0), negative (labeled 1), neutral (labeled 2), positive (labeled 3), and very positive (labeled 4) [126]. The dataset is a binary dataset if it considers only positive and negative categories.