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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
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