Recent advancements in computational morphology : A comprehensive survey

TL;DR

This comprehensive survey reviews the evolution of computational morphology methods, from traditional techniques to modern neural network approaches, highlighting datasets, challenges, and future research directions in NLP word processing.

Contribution

It provides an exhaustive overview of methods, datasets, and challenges in computational morphology, emphasizing the transition to neural network-based approaches.

Findings

Neural models outperform traditional methods in accuracy.

A wide range of datasets are available across languages.

Open challenges include handling low-resource languages.

Abstract

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.