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 language | English |
|---|---|
| Title of host publication | 2019 International Joint Conference on Neural Networks, IJCNN 2019 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781728119854 |
| DOIs | |
| Publication status | Published - 2019 Jul |
| Event | 2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary Duration: 2019 Jul 14 → 2019 Jul 19 |
Publication series
| Name | Proceedings of the International Joint Conference on Neural Networks |
|---|---|
| Volume | 2019-July |
Conference
| Conference | 2019 International Joint Conference on Neural Networks, IJCNN 2019 |
|---|---|
| Country | Hungary |
| City | Budapest |
| Period | 19/7/14 → 19/7/19 |
Fingerprint
Keywords
- Encoder-Decoder model
- Mongolian
- Morphological Segmentation
ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Neural Morphological Segmentation Model for Mongolian. / Wang, Weihua; Fam, Rashel; Bao, Feilong; Lepage, Yves; Gao, Guanglai.
2019 International Joint Conference on Neural Networks, IJCNN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8852050 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neural Morphological Segmentation Model for Mongolian
AU - Wang, Weihua
AU - Fam, Rashel
AU - Bao, Feilong
AU - Lepage, Yves
AU - Gao, Guanglai
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.
ER -