A Survey of Numerous Text Similarity Approach

Abstract

One of the most common NLP use cases is text similarity. Every domain comes with a variety of use cases. The most common uses of text similarity include finding related articles/news/genres, efficient use of search engines, classification of related issues on any topic, etc. It serves as a framework for many text analytics use cases. Methods to solve text similarity use cases have been around for a while, but the main drawbacks of the old methods are loss of dependency information, difficulty remembering long conversations, exploding gradient problems, etc. Recent advanced deep learning-based models pay attention to both contiguous and distant words, making their learning ability more rigorous. This white paper focuses on various text similarity techniques that can be used in everyday life to solve these use cases.