Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/135750
題名: | Supervised Word Sense Disambiguation on Polysemy with Neural Network Models: A Case Study of BUN in Taiwan Hakka | 作者: | 賴惠玲 Lai, Huei-ling Zhao, Peilian Mao, Cunli Yu, Zhengtao |
貢獻者: | 英文系 | 關鍵詞: | Semi-supervised learning ; sentiment analysis ; knowledge graph embedding | 日期: | Jan-2020 | 上傳時間: | 11-Jun-2021 | 摘要: | 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. | 關聯: | International Journal of Asian Language Processing, Vol. 30, No. 03, 2050012 (2020) | 資料類型: | article | DOI: | https://doi.org/10.1142/S2717554520500125 |
Appears in Collections: | 期刊論文 |
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.