Neural Morphological Segmentation Model for Mongolian

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

研究成果: Conference contribution

抜粋

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.

元の言語English
ホスト出版物のタイトル2019 International Joint Conference on Neural Networks, IJCNN 2019
出版者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728119854
DOI
出版物ステータスPublished - 2019 7
イベント2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
継続期間: 2019 7 142019 7 19

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
Hungary
Budapest
期間19/7/1419/7/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

これを引用

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