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Hierarchical Representation and Label Embedding for Semantic Classification of Domestic Research Paper
Heejin Kook, Yeonghwa Kim, Sehui Yoon, Byungha Kang, Youhyun Shin
http://doi.org/10.5626/JOK.2024.51.1.41
The sentence"s meaning in the paper is that it has a hierarchical structure, and there is data imbalance among subcategories. In addition, the meaning of the sentence in the paper is closely related to its position within the paper. Existing flat classification methods mainly consider only subcategories, leading to a decrease in classification accuracy due to data imbalance. In response to this, this study proposes hierarchical representation and label embedding methods to perform hierarchical semantic classification of sentences effectively. In addition, the section names of the paper are actively utilized to represent the positional information of the paper sentences. Through experiments, it is demonstrated that the proposed method, which explicitly considers hierarchical and positional information in the KISTI domestic paper sentence semantic tagging dataset, achieves excellent performance in terms of F1 score.
Review-based Personalized Recommendation System using Effective Personalized Fusion and BERT
http://doi.org/10.5626/JOK.2023.50.8.646
Generally, review texts contain personal information from users, and reviews written by users can have different meanings, even if they use the exact wording. These review features can be used to compensate for the shortcomings of collaborative filtering, which is vulnerable to data sparsity. They can also be used as information for personalized recommendation systems. Despite the success of pre-trained language models in natural language processing, there has been little research on personalized recommendation systems that leverage BERT to enrich individual user features from reviews. In this work, we propose a rating prediction model that uses BERT for detailed learning of user and item-specific features from reviews and tightly combine them with user and product IDs to represent personalized user and item. Experiments results show that the proposed model can achieve improved performance over the baseline on the Amazon benchmark dataset.
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