Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/135750
DC Field | Value | Language |
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dc.contributor | 英文系 | |
dc.creator | 賴惠玲 | |
dc.creator | Lai, Huei-ling | |
dc.creator | Zhao, Peilian | |
dc.creator | Mao, Cunli | |
dc.creator | Yu, Zhengtao | |
dc.date | 2020-01 | |
dc.date.accessioned | 2021-06-11T01:46:56Z | - |
dc.date.available | 2021-06-11T01:46:56Z | - |
dc.date.issued | 2021-06-11T01:46:56Z | - |
dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/135750 | - |
dc.description.abstract | Aspect-Based Sentiment Analysis (ABSA), a fine-grained task of opinion mining, which aims to extract sentiment of specific target from text, is an important task in many real-world applications, especially in the legal field. Therefore, in this paper, we study the problem of limitation of labeled training data required and ignorance of in-domain knowledge representation for End-to-End Aspect-Based Sentiment Analysis (E2E-ABSA) in legal field. We proposed a new method under deep learning framework, named Semi-ETEKGs, which applied E2E framework using knowledge graph (KG) embedding in legal field after data augmentation (DA). Specifically, we pre-trained the BERT embedding and in-domain KG embedding for unlabeled data and labeled data with case elements after DA, and then we put two embeddings into the E2E framework to classify the polarity of target-entity. Finally, we built a case-related dataset based on a popular benchmark for ABSA to prove the efficiency of Semi-ETEKGs, and experiments on case-related dataset from microblog comments show that our proposed model outperforms the other compared methods significantly. | |
dc.format.extent | 178928 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.relation | International Journal of Asian Language Processing, Vol. 30, No. 03, 2050012 (2020) | |
dc.subject | Semi-supervised learning ; sentiment analysis ; knowledge graph embedding | |
dc.title | Supervised Word Sense Disambiguation on Polysemy with Neural Network Models: A Case Study of BUN in Taiwan Hakka | |
dc.type | article | |
dc.identifier.doi | 10.1142/S2717554520500125 | |
dc.doi.uri | https://doi.org/10.1142/S2717554520500125 | |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | restricted | - |
item.openairetype | article | - |
item.cerifentitytype | Publications | - |
Appears in Collections: | 期刊論文 |
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