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.

该研究综述了发展计算形态学相关工具的方法，从传统方法到基于深度神经网络的新方法的历史文献进行了调查，讨论了神经模型与传统模型的有效性，并提出了构建计算形态学工具所面临的独特挑战，最后讨论了该领域中的一些最新研究问题。

计算形态学的最新进展：综述