Optimize the Learning Rate of Neural Architecture in Myanmar Stemmer
Yadanar Oo, Khin Mar Soe · International Journal on Natural Language Computing · 2019
Morphological stemming becomes a critical step toward natural language processing.The process of stemming is to reduce alternative forms to a common morphological root.Word segmentation for Myanmar Language, like for most Asian Languages, is an important task and extensively-studied sequence labelling problem.Named entity detection is one of the issues in Asian Language that has traditionally required a large amount of feature engineering to achieve high performance.The new approach is integrating them that would benefit in all these processes.In recent years, end-to-end sequence labelling models with deep learning are widely used.This paper introduces a deep BiGRU-CNN-CRF network that jointly learns word segmentation, stemming and named entity recognition tasks.We trained the model using manually annotated corpora.State-of-the-art named entity recognition systems rely heavily on handcrafted feature built in our new approach, we introduce the joint model that relies on two sources of information: character level representation and syllable level representation.