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https://elib.vku.udn.vn/handle/123456789/2725Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ye, Kyaw Thu | - |
| dc.contributor.author | Thura, Aung | - |
| dc.contributor.author | Thepchai, Supnithi | - |
| dc.date.accessioned | 2023-09-26T01:36:49Z | - |
| dc.date.available | 2023-09-26T01:36:49Z | - |
| dc.date.issued | 2023-07 | - |
| dc.identifier.isbn | 978-3-031-36886-8 | - |
| dc.identifier.uri | https://link.springer.com/chapter/10.1007/978-3-031-36886-8_24 | - |
| dc.identifier.uri | http://elib.vku.udn.vn/handle/123456789/2725 | - |
| dc.description | Lecture Notes in Networks and Systems (LNNS, volume 734); CITA: Conference on Information Technology and its Applications; pp: 285-296. | vi_VN |
| dc.description.abstract | In the informal Myanmar language, for which most NLP applications are used, there is no predefined rule to mark the end of the sentence. Therefore, in this paper, we contributed the first Myanmar sentence segmentation corpus and systematically experimented with twelve neural sequence labeling architectures trained and tested on both sentence and sentence+paragraph data. The word LSTM + Softmax achieved the highest accuracy of 99.95% while trained and tested on sentence-only data and 97.40% while trained and tested on sentence + paragraph data. | vi_VN |
| dc.language.iso | en | vi_VN |
| dc.publisher | Springer Nature | vi_VN |
| dc.subject | Sentence Segmentation | vi_VN |
| dc.subject | Neural Sequence Labeling | vi_VN |
| dc.subject | Myanmar language | vi_VN |
| dc.subject | CRF | vi_VN |
| dc.subject | NCRF++ | vi_VN |
| dc.subject | CNN | vi_VN |
| dc.subject | Bi-LSTM | vi_VN |
| dc.title | Neural Sequence Labeling Based Sentence Segmentation for Myanmar Language | vi_VN |
| dc.type | Working Paper | vi_VN |
| Appears in Collections: | CITA 2023 (International) | |
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