Data for our AAAI'19 paper "Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification".
This dataset acts as highly beneficial source domains to improve the learning of more fine-grained aspect-term level (AT) sentiment analysis. The dataset has three characteristics:
Even with a simple attention-based model for the AT task, our method can achieve the STOA performances by leveraging the knowledge distilled from the AC task.
Each instance behaves as the format below:
inst1: ID1/sentence1/aspect1/label1
inst2: ID1/sentence1/aspect2/label2
inst3: ID1/sentence1/aspect3/label3
....
The sentence containing multiple aspects are arranged together.
positive: 1 neutral: 0 negative: -1
If the data is useful for your research, please be kindly to give us stars and cite our paper as follows:
@article{li2018exploiting,
title={Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification},
author={Li, Zheng and Wei, Ying and Zhang, Yu and Zhang, Xiang and Li, Xin and Yang, Qiang},
journal={arXiv preprint arXiv:1811.10999},
year={2018}
}