# Short Text Clustering Algorithms, Application and Challenges: A Survey
## 3 Components of Short Text Clustering
### Document Representation  
Even after the noise in the text has been removed during pre-processing, the text still does not fit together well enough to produce the best results when clustering. Therefore, focusing on the text representation step is essential, which involves converting the word or the full text from its initial form into another. Directly applying learning algorithms to text information without representing it is impossible [62] because text information has complex nature [63]. Textual document content must be converted into a concise representation before applying a machine learning approach to the text. Language-independent approaches are particularly successful because they are not dependent on the meaning of the language and perform well in the event of noisy text. As these methods do not depend on language, they are efficient [64].  
Short text similarity has attracted more attention in recent years, and understanding semantics correctly between documents is challenging to understanding lexical diversity and ambiguity [65]. Representing short text is critical in NLP yet challenging owing to its sparsity; high dimensionality; complexity; large volume and much irrelevant, redundant and noisy information [1; 66]. As a result, the traditional methods of computing semantic similarity are a significant roadblock because they are ineffective in various circumstances. Many existing traditional systems fail to deal with terms not covered by synonyms and cannot handle abbreviations, acronyms, brand names and other terms [67]. Examples of these traditional systems are BOW and TF-IDF, which represent text as real value vectors to help with semantic similarity computation. However, these strategies cannot account for the fact that words have diverse meanings and that different words may be used to represent the same concept. For example, consider two sentences: '[PERSON] is taking insulin' and '[PERSON] has diabetes'. Although these two sentences have the same meaning, they do not use the same words. These methods capture the lexical features of the text and are simple to implement; however, they ignore the semantic and syntactic features of the text. To address this issue,  
Figure 6: Example of stemming.  
several studies have expanded and enriched the context of data from an ontology [68; 69] or Wikipedia [70; 71]. However, these techniques require a great deal of understanding of NLP. They still use high-dimensional representation for short text, which may lead to wasting memory and computing time. Generally, these methods use external knowledge to improve contextual information for short texts. Many short text similarity measurement approaches exist, such as representation-based measurement [72; 73], which learn new representations for short text and then measure similarity based on this model [74]. A large number of similarity metrics have previously been proposed in the literature. We choose corpus-based and knowledge-based metrics because of their observed performance in NLP applications. This study explains several representation-based measurement methods, as shown in Figure 7.