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2022 NAACL NAACL 2022

JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morphological Segmentation

Abstract

AbstractThis paper describes the JB132 submission to the SIGMORPHON 2022 Shared Task 3 on Morpheme Segmentation. In this paper we describe probabilistic model trained with the Expectation-Maximization algorithm, we provide the results and analyze sources of errors and general limitations of our approach. The model was implemented within our own modular probabilistic framework.

🐝 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, Speech & Audio

Authors

Jan Bodnár

Topics

Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Optimization & Theory > Stochastic Processes

Keywords

expectation maximization probabilistic model morphological segmentation language processing
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