Computational Morphology with Neural Network Approaches

2021/05/19 by Ling Liu, Liu, Ling · 1 citation
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Rough Sets and Fuzzy Logic #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2105.09404

openalex publication_date 2021/05/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Neural network approaches have been applied to computational morphology with great success, improving the performance of most tasks by a large margin and providing new perspectives for modeling. This paper starts with a brief introduction to computational morphology, followed by a review of recent work on computational morphology with neural network approaches, to provide an overview of the area. In the end, we will analyze the advantages and problems of neural network approaches to computational morphology, and point out some directions to be explored by future research and study.

Citations

• Learning to Search Better Than Your Teacher
• Sequence to Sequence Learning with Neural Networks
• Bidirectional recurrent neural networks
• On the Diachronic Stability of Irregularity in Inflectional Morphology
• A Reduction of Imitation Learning and Structured Prediction to No-Regret\n Online Learning

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