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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 2020-01
上傳時間 11-Jun-2021 09:46:56 (UTC+8)
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
dc.contributor 英文系
dc.creator (作者) 賴惠玲
dc.creator (作者) Lai, Huei-ling
dc.creator (作者) Zhao, Peilian
dc.creator (作者) Mao, Cunli
dc.creator (作者) Yu, Zhengtao
dc.date (日期) 2020-01
dc.date.accessioned 11-Jun-2021 09:46:56 (UTC+8)-
dc.date.available 11-Jun-2021 09:46:56 (UTC+8)-
dc.date.issued (上傳時間) 11-Jun-2021 09:46:56 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/135750-
dc.description.abstract (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.
dc.format.extent 178928 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) International Journal of Asian Language Processing, Vol. 30, No. 03, 2050012 (2020)
dc.subject (關鍵詞) Semi-supervised learning ; sentiment analysis ; knowledge graph embedding
dc.title (題名) Supervised Word Sense Disambiguation on Polysemy with Neural Network Models: A Case Study of BUN in Taiwan Hakka
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1142/S2717554520500125
dc.doi.uri (DOI) https://doi.org/10.1142/S2717554520500125