Hsiu-Min Chuang


2021

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Home Appliance Review Research Via Adversarial Reptile
Tai-Jung Kan | Chia-Hui Chang | Hsiu-Min Chuang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

For manufacturers of home appliances, the Studying discussion of products on social media can help manufacturers improve their products. Opinions provided through online reviews can immediately reflect whether the product is accepted by people, and which aspect of the product are most discussed . In this article, we divide the analysis of home appliances into three tasks, including named entity recognition (NER), aspect category extraction (ACE), and aspect category sentiment classification (ACSC). To improve the performance of ACSC, we combine the Reptile algorithm in meta learning with the concept of domain adversarial training to form the concept of the Adversarial Reptile algorithm. We find show that the macro-f1 is improved from 68.6% (BERT fine tuned model) to 70.3% (p-value 0.04).

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Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning
Hsiao-Wen Tseng | Chia-Hui Chang | Hsiu-Min Chuang
Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)

When we are interested in a certain domain, we can collect and analyze data from the Internet. The newly collected data is not labeled, so the use of labeled data is hoped to be helpful to the new data. We perform name entity recognition (NER) and aspect-based sentiment analysis (ABSA) in multi-task learning, and combine parameter generation network and DANN architecture to build the model. In the NER task, the data is labeled with Tie, Break, and the task weight is adjusted according to the loss change rate of each task using Dynamic Weight Average (DWA). This study used two different source domain data sets. The experimental results show that Tie, Break can improve the results of the model; DWA can have better performance in the results; the combination of parameter generation network and gradient reversal layer can be used for every good learning in different domain.

2017

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應用興趣點辨識技術從 Web 中挖掘新商家資訊 (Mining POIs from Web via POI recognition and Relation Verification) [In Chinese]
Kuo-Hsin Hsu | Hsiu-Min Chuang | Chien-Lung Chou | Chia-Hui Chang
Proceedings of the 29th Conference on Computational Linguistics and Speech Processing (ROCLING 2017)

2015

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基於Web之商家景點擷取與資料庫建置(Points of Interest Extraction from Unstructured Web)[In Chinese]
Ting-Yao Kao | Hsiu-Min Chuang | Chia-Hui Chang
Proceedings of the 27th Conference on Computational Linguistics and Speech Processing (ROCLING 2015)