Neural Morphological Segmentation Model for Mongolian

Weihua Wang, Rashel Fam, Feilong Bao, Yves Lepage, Guanglai Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Morphological segmentation is useful for processing Mongolian. In this paper, we manually build a morphological segmentation data set for Mongolian. We then present a character-based encoder-decoder model with attention mechanism to perform the morphological segmentation task. We further investigate the influence of analogy features extracted from scratch and improve the performance of our model using multi languages setting. Experimental results show that our encoder-decoder model with attention mechanism provides a strong baseline for Mongolian morphological segmentation. The analogy features provide useful information to the model and improve the performance of the system. The use of multi languages data set shows the capability of our model to acquire knowledge through different languages and delivers the best result.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - 2019 Jul
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 2019 Jul 142019 Jul 19

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period19/7/1419/7/19

Keywords

  • Encoder-Decoder model
  • Mongolian
  • Morphological Segmentation

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Wang, W., Fam, R., Bao, F., Lepage, Y., & Gao, G. (2019). Neural Morphological Segmentation Model for Mongolian. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852050] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852050