  Computational morphology handles the language processing at the word level.
It is one of the foundational tasks in the NLP pipeline for the development of
higher level NLP applications. It mainly deals with the processing of words and
word forms. Computational Morphology addresses various sub problems such as
morpheme boundary detection, lemmatization, morphological feature tagging,
morphological reinflection etc. In this paper, we present exhaustive survey of
the methods for developing computational morphology related tools. We survey
the literature in the chronological order starting from the conventional
methods till the recent evolution of deep neural network based approaches. We
also review the existing datasets available for this task across the languages.
We discuss about the effectiveness of neural model compared with the
traditional models and present some unique challenges associated with building
the computational morphology tools. We conclude by discussing some recent and
open research issues in this field.
