Yuki Nakayama


A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis
Yuki Nakayama | Koji Murakami | Gautam Kumar | Sudha Bhingardive | Ikuko Hardaway
Proceedings of the Thirteenth Language Resources and Evaluation Conference

There has been significant progress in the field of sentiment analysis. However, aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language even though it has a huge scope in many natural language processing applications such as 1) tracking sentiment towards products, movies, politicians etc; 2) improving customer relation models. The main reason behind this is that there is no standard Japanese dataset available for ABSA task. In this paper, we present the first standard Japanese dataset for the hotel reviews domain. The proposed dataset contains 53,192 review sentences with seven aspect categories and two polarity labels. We perform experiments on this dataset using popular ABSA approaches and report error analysis. Our experiments show that contextual models such as BERT works very well for the ABSA task in the Japanese language and also show the need to focus on other NLP tasks for better performance through our error analysis.

A Stacking-based Efficient Method for Toxic Language Detection on Live Streaming Chat
Yuto Oikawa | Yuki Nakayama | Koji Murakami
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track

In a live streaming chat on a video streaming service, it is crucial to filter out toxic comments with online processing to prevent users from reading comments in real-time.However, recent toxic language detection methods rely on deep learning methods, which can not be scalable considering inference speed.Also, these methods do not consider constraints of computational resources expected depending on a deployed system (e.g., no GPU resource).This paper presents an efficient method for toxic language detection that is aware of real-world scenarios.Our proposed architecture is based on partial stacking that feeds initial results with low confidence to meta-classifier.Experimental results show that our method achieves a much faster inference speed than BERT-based models with comparable performance.


Extracting Condition-Opinion Relations Toward Fine-grained Opinion Mining
Yuki Nakayama | Atsushi Fujii
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing


Extracting Evaluative Conditions from Online Reviews: Toward Enhancing Opinion Mining
Yuki Nakayama | Atsushi Fujii
Proceedings of the Sixth International Joint Conference on Natural Language Processing