[AAAI 2019] A Unified Model for Opinion Target Extraction and Target Sentiment Prediction
Source code of our AAAI paper on End-to-End Target/Aspect-Based Sentiment Analysis.
rest_total
dataset built by ourselves again, more details can be found in Updated Results.config.py
.The data files of the rest_total
dataset are created by concatenating the train/test counterparts from rest14
, rest15
and rest16
and our motivation is to build a larger training/testing dataset to stabilize the training & faithfully reflect the capability of the ABSA model. However, we recently found that the SemEval organizers directly treat the union set of rest15.train
and rest15.test
as the training set of rest16 (i.e., rest16.train
), and thus, there exists overlap between rest_total_train.txt
and rest_total_test.txt
, which makes this dataset invalid. When you follow our works on this E2E-ABSA task, we hope you DO NOT use this rest_total
dataset any more but change to the officially released rest14
, rest15
and rest16
. We have prepared data files with train/dev/test split in our another project, check it out if needed.
To facilitate the comparison in the future, we re-run our models following the settings in config.py
and report the results (micro-averaged F1) on rest14
, rest15
and rest16
:
Model | rest14 | rest15 | rest16 |
---|---|---|---|
E2E-ABSA (OURS) | 67.10 | 57.27 | 64.31 |
(He et al., 2019) | 69.54 | 59.18 | - |
(Liu et al., 2020) | 68.91 | 58.37 | - |
BERT-Linear (OURS) | 72.61 | 60.29 | 69.67 |
BERT-GRU (OURS) | 73.17 | 59.60 | 70.21 |
BERT-SAN (OURS) | 73.68 | 59.90 | 70.51 |
BERT-TFM (OURS) | 73.98 | 60.24 | 70.25 |
BERT-CRF (OURS) | 73.17 | 60.70 | 70.37 |
(Chen and Qian, 2020) | 75.42 | 66.05 | - |
(Liang et al., 2020) | 72.60 | 62.37 | - |
If the code is used in your research, please star this repo and cite our paper as follows:
@inproceedings{li2019unified,
title={A unified model for opinion target extraction and target sentiment prediction},
author={Li, Xin and Bing, Lidong and Li, Piji and Lam, Wai},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
pages={6714--6721},
year={2019}
}