Interactive Neural Network: Leveraging Part-of-Speech Window for Aspect Term Extraction (Student Abstract)
Abstract
Aspect term extraction is a fundamental task for aspect-level sentiment analysis. Previous methods tend to extract noun aspect terms due to the large quantities of them, and perform badly on extracting aspect terms containing words with other POS tags, according to experimental results. In addition, few works focus on the POS tags of adjacent words which are critical to aspect term extraction. We propose a novel model which combines POS and word features in an interactive way, and makes full use of the POS tags of adjacent words by POS window. We conduct experiments on two datasets, and prove the effectiveness of our model.
Cite
Text
Yin et al. "Interactive Neural Network: Leveraging Part-of-Speech Window for Aspect Term Extraction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7261Markdown
[Yin et al. "Interactive Neural Network: Leveraging Part-of-Speech Window for Aspect Term Extraction (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/yin2020aaai-interactive/) doi:10.1609/AAAI.V34I10.7261BibTeX
@inproceedings{yin2020aaai-interactive,
title = {{Interactive Neural Network: Leveraging Part-of-Speech Window for Aspect Term Extraction (Student Abstract)}},
author = {Yin, Da and Wu, Xiuyu and Chang, Baobao},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13977-13978},
doi = {10.1609/AAAI.V34I10.7261},
url = {https://mlanthology.org/aaai/2020/yin2020aaai-interactive/}
}