We approach comparative opinion classification through prompt learning, taking the advantage of embedded knowledge in pre-trained language model. We design a twin framework with dual prompts, named CORT. This extremely simple model delivers state-of-the-art and robust performance on all benchmark datasets for comparative opinion classification. We believe CORT well serves as a new baseline for comparative opinion classification.

We propose the Context and Former-Label Enhanced Net (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets and one proprietary dataset, we show that our achieves state-of-the-art performance on complicated quotation extraction.

We propose a novel framework to tackle the problem of packet representation learning for various traffic classification tasks. We learn packet representation, preserving both semantic and byte patterns of each packet, and utilize contrastive loss with a sample selector to optimize the learned representations so that similar packets are closer in the latent semantic space.

In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately. Based on this dual-channel framework, we design the Dual-Channel Network (DC-Net) to recognize sentiment conflict.

In this paper, we propose the aspect-level sentiment capsules model (AS-Capsules), which is capable of performing aspect detection and sentiment classification simultaneously, in a joint manner.

In this paper, we propose RNN-Capsule, a capsule model based on Recurrent Neural Network (RNN) for sentiment analysis.

To reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect, we propose an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification.