Abstract
In the NLP context identification has become a prominent way to overcome syntactic and semantic ambiguities. Ambiguities are unsolved problems but can be reduced to a certain level. This ambiguity reduction helps to improve the quality of several NLP processes, such as text translation, text simplification, text retrieval, word sense disambiguation, etc. Context identification, also known as contextualization, takes place in the preprocessing phase of NLP processes. The essence of this identification is to uniquely represent a word or a phrase to improve the decision-making during the transfer phase of the NLP processes. The improved decision-making helps to improve the quality of the output. This paper tries to provide an overview of different context-capturing mechanisms used in NLP.
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