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The problem of resolving references to earlier or later items in the discourse is commonly called as anaphora resolution or pronoun resolution. These items are usually noun phrases representing objects in the real world called referents but can also be verb phrases, whole sentences or paragraphs. Nowadays, anaphora resolution is addressed in numerous NLP (Natural Language Processing) applications. Proper treatment of anaphoric relations improves the performance of applications. Machine translation, information extraction, text summarization, or dialogue systems are some of the common applications of NLP. In early days, the machine translation systems processed on the basis of a sentence-by-sentence level. It did not consider the ties between sentences and resulted in an incoherent text as output. When the researcher forgets to handle the anaphora issue, it results in the striking problem of incorrect facts. It is very much needed to concentrate on the usage of pronoun, as it should match with their antecedents both in number and gender. Assigning inappropriate morphological features to the anaphor often may also lead to an undesirable change in the meaning of the sentence.

Need for Anaphoric Resolution towards Sentiment Analysis-A Case Study with Scarlet Pimpernel (Novel)