mlsanityData to 7 Oct 2026

Recent advancements in computational morphology : A comprehensive survey

arXiv 2406.05424 ↗First posted 8 Jun 2024cs.CL cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 40 of its 133 references that are in the map. First posted on arXiv 8 Jun 2024.

Builds on

40 of 133 references.

References · 40

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28 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?cited by 10
  2. 2026Comonadic Morphophonology: A Compositional Framework for Context-Dependent Morphological Rules in Finnishcited by 0