Computational Morphology with Neural Network Approaches
This is an incremental review paper that synthesizes existing work for researchers in computational linguistics and morphology.
The paper reviews neural network approaches in computational morphology, highlighting their success in improving task performance and offering new modeling perspectives, but does not present new experimental results or specific numerical gains.
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.