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Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/135750
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| Title: | Supervised Word Sense Disambiguation on Polysemy with Neural Network Models: A Case Study of BUN in Taiwan Hakka |
| Authors: | 賴惠玲
Lai, Huei-ling
Zhao, Peilian
Mao, Cunli
Yu, Zhengtao |
| Contributors: | 英文系 |
| Keywords: | Semi-supervised learning;sentiment analysis;knowledge graph embedding |
| Date: | 2020-01 |
| Issue Date: | 2021-06-11 09:46:56 (UTC+8) |
| 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. |
| Relation: | International Journal of Asian Language Processing, Vol. 30, No. 03, 2050012 (2020) |
| Data Type: | article |
| DOI 連結: | https://doi.org/10.1142/S2717554520500125 |
| DOI: | 10.1142/S2717554520500125 |
| Appears in Collections: | [英國語文學系] 期刊論文
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