Modelling Text Similarity: A Survey

Online social networking services such as Twitter and Instagram have become pervasive platforms for engaging in discussions on a wide array of topics. These platforms cater to both mainstream subjects, like music and movies, as well as more specialized areas, such as politics. With the growing volume of textual data generated on these platforms, the ability to define and identify similar texts becomes crucial for effective investigation and clustering. In this paper, we explore the challenges and significance of text similarity regression models in the context of online social networking services. We delve into the methods and techniques employed to define and find similarities among texts, enabling the extraction of meaningful patterns and insights. Specifically, we categorize text similarity regression models into four distinct types: set-theoretic, sequence-theoretic, real-vector, and end-to-end methods. This categorization is based on the mathematical formalisation of similarity used by each model. Ultimately, our survey aims to provide a comprehensive overview of the interlinkages between independently proposed methods for text similarity. By understanding the strengths and weaknesses of these methods, researchers can make informed decisions when designing novel approaches and algorithms. We hope this survey serves as a valuable resource for advancing the state-of-the-art in addressing the complex problem of text similarity.