# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 1 Introduction
### Barriers and Drawbacks of Short Sentences in Semantic Similarity  
Figure 1 presents the numerous barriers and drawbacks of short sentences, which degrade the performance of machine learning algorithms.  
Ambiguity: Conventional methods, which include parsing and parts-of-speech labeling, cannot be used for short text, because it does not adhere to appropriate syntax in any natural language. Short texts have a very limited amount of information, and most responses in short sentences are five words; however, search queries and tweets are limited to 140 characters [6; 7; 8; 9]. Due to this, the short texts lack adequate statistical signals to support text processing. These problems lead the short text toward sentence ambiguity; in addition, multiple new approaches are required to solve these issues.  
Polysemy and Sparsity: Social networks, search engines, blogs, and various other platforms produce a huge amount of short text, which is often known as sparse data. The data is presented in the form of synonyms and polysemous terms. Polysemy refers to a term or phrase with several meanings. However, the word polysemy is often related to linguistics, which presents sophisticated issues and vague concepts in short text messages and snippets. In natural language processing, word sense disambiguation (WSD) is usually used to solve the problem of synonym and polysemy [10]. Moreover, the lexical meaning of word representations in natural language processing and philosophical contexts is difficult to handle for machine learning algorithms.  
Anomalies: Earlier projects failed to ensure maximum correctness in the domain of short text, due to the fact that a lot of content is partially available. Short text produces a certain degree of anomalies, such as abbreviations, non-standard terms, misspellings, improper punctuations, grammatical errors, and slang words [11]. Machine learning needs an appropriate strategy to solve these irregularities in a short text.  
Sentence Structure: In the short text, there are two components that structure the sentences, including (i) punctuation and (ii) word order. Inappropriate punctuation makes the sentences more complicated to understand, whereas every word includes different meanings that influence the contextual meaning of the text. There are other factors that affect the short text, including sentence fragments and run-on sentences.  
Run-on sentences: These sentences use incorrect exclamation marks to form the sections of sentences.  
Sentence fragments: Sentence fragments, which lack the necessary details to complete the sentence (one simple example is a predicate) and many which do not include the main verb, including only noun phrases, cannot complete the sentences [10; 11]. However, in  
Figure 1: Drawbacks of short sentences.  
short sentences, grammar is not the only factor that affects the structure. Style and rhythm are also key factors that make short sentences more challenging for machine learning algorithms. Moreover, machine learning algorithms use preprocessing pipelines on more sophisticated sentences. Under natural language processing, preprocessing techniques (Figure 2) help to identify the actual text from unstructured raw text.