  Estimating the semantic similarity between text data is one of the
challenging and open research problems in the field of Natural Language
Processing (NLP). The versatility of natural language makes it difficult to
define rule-based methods for determining semantic similarity measures. In
order to address this issue, various semantic similarity methods have been
proposed over the years. This survey article traces the evolution of such
methods, categorizing them based on their underlying principles as
knowledge-based, corpus-based, deep neural network-based methods, and hybrid
methods. Discussing the strengths and weaknesses of each method, this survey
provides a comprehensive view of existing systems in place, for new researchers
to experiment and develop innovative ideas to address the issue of semantic
similarity.
