TY - GEN
T1 - Neural Morphological Segmentation Model for Mongolian
AU - Wang, Weihua
AU - Fam, Rashel
AU - Bao, Feilong
AU - Lepage, Yves
AU - Gao, Guanglai
N1 - Funding Information:
This work was funded in part by National Natural Science Foundation of China (No.61773224 and No.61563040), Natural Science Foundation of Inner Mongolia (No. 2016ZD06). We would also like to thank the anonymous reviewers for their useful comments.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Encoder-Decoder model
KW - Mongolian
KW - Morphological Segmentation
UR - http://www.scopus.com/inward/record.url?scp=85073235315&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073235315&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2019.8852050
DO - 10.1109/IJCNN.2019.8852050
M3 - Conference contribution
AN - SCOPUS:85073235315
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2019 International Joint Conference on Neural Networks, IJCNN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Joint Conference on Neural Networks, IJCNN 2019
Y2 - 14 July 2019 through 19 July 2019
ER -