Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?

Alexey Sorokin

2019 ACL ACL 2019

Convolutional neural networks for low-resource morpheme segmentation: baseline or state-of-the-art?

Abstract

AbstractWe apply convolutional neural networks to the task of shallow morpheme segmentation using low-resource datasets for 5 different languages. We show that both in fully supervised and semi-supervised settings our model beats previous state-of-the-art approaches. We argue that convolutional neural networks reflect local nature of morpheme segmentation better than other semi-supervised approaches.

❓ The Questioner

🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Alexey Sorokin

Topics

Machine Learning > Learning Types > Semi-Supervised Learning Natural Language Processing > Applications > Information Extraction Deep Learning > Architectures > Convolutional Neural Networks

Keywords

semi-supervised learning feature learning low-resource language convolutional neural network morpheme segmentation

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