Search Results for author: Fei Zhao

Found 5 papers, 3 papers with code

Analysing Wideband Absorbance Immittance in Normal and Ears with Otitis Media with Effusion Using Machine Learning

no code implementations4 Mar 2021 Emad M. Grais, Xiaoya Wang, Jie Wang, Fei Zhao, Wen Jiang, Yuexin Cai, Lifang Zhang, Qingwen Lin, Haidi Yang

Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results.

Attention Transfer Network for Aspect-level Sentiment Classification

1 code implementation COLING 2020 Fei Zhao, Zhen Wu, Xinyu Dai

In neural network-based methods for ASC, most works employ the attention mechanism to capture the corresponding sentiment words of the opinion target, then aggregate them as evidence to infer the sentiment of the target.

Classification Document-level +2

Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction

2 code implementations Findings of the Association for Computational Linguistics 2020 Zhen Wu, Chengcan Ying, Fei Zhao, Zhifang Fan, Xinyu Dai, Rui Xia

To validate the feasibility and compatibility of GTS, we implement three different GTS models respectively based on CNN, BiLSTM, and BERT, and conduct experiments on the aspect-oriented opinion pair extraction and opinion triplet extraction datasets.

Aspect Sentiment Triplet Extraction

Latent Opinions Transfer Network for Target-Oriented Opinion Words Extraction

1 code implementation7 Jan 2020 Zhen Wu, Fei Zhao, Xin-yu Dai, Shu-Jian Huang, Jia-Jun Chen

In this paper, we propose a novel model to transfer these opinions knowledge from resource-rich review sentiment classification datasets to low-resource task TOWE.

Aspect-oriented Opinion Extraction General Classification +1

Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation

no code implementations21 Oct 2019 Emad M. Grais, Fei Zhao, Mark D. Plumbley

In the spectrogram of a mixture of singing voices and music signals, there is usually more information about the voice in the low frequency bands than the high frequency bands.

Dimensionality Reduction

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