Recent advancements in computational morphology : A comprehensive survey
This is an incremental survey paper that synthesizes existing knowledge for researchers in NLP, without introducing new methods or results.
This paper provides a comprehensive survey of computational morphology, covering methods from conventional approaches to recent deep neural network-based techniques, and reviews datasets across languages while discussing challenges and open research issues.
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.