CLE4ATE

Constituency Lattice Encoding for Aspect Term Extraction. Yunyi Yang, Kun Li, Xiaojun Quan, Weizhou Shen, Qinliang Su. In Proceedings of COLING, 2020.

Data

[Laptop] [Restaurant 16]: the constituency parsing results has been already provided in the processed data.

Requirements

• pytorch=1.3.1
• python=3.7.5
• transformers=2.3.0
• dgl=0.5

Steps to Run Code

• Step 1:

Download official datasets and official evaluation scripts. We assume the following file names. SemEval 2014 Laptop (http://alt.qcri.org/semeval2014/task4/):

semeval/Laptops_Test_Data_PhaseA.xml
semevalLaptops_Test_Gold.xml
semeval/eval.jar

SemEval 2016 Restaurant (http://alt.qcri.org/semeval2016/task5/)

semeval/EN_REST_SB1_TEST.xml.A
semeval/EN_REST_SB1_TEST.xml.gold
semeval/A.jar

• Step 2:

Download pre-trained model weight [BERT-PT], and place these files as:

bert-pt/bert-laptop/
bert-pt/bert-rest/

you can also specify the address of these files in config.json.

• Step 3:

Train and evaluate:

sh train.sh

Citation

If you used the datasets or code, please cite our paper:

@inproceedings{yang-etal-2020-constituency,
    title = "Constituency Lattice Encoding for Aspect Term Extraction",
    author = "Yang Yunyi, Li Kun, Quan Xiaojun, Shen Weizhou and Su Qinliang",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.coling-main.73",
    doi = "10.18653/v1/2020.coling-main.73",
    pages = "844--855"
}

Reference

[1]. Hu Xu, Bing Liu, Lei Shu, Philip Yu. Bert post-training for review reading comprehension and aspect-based sentiment analysis. In Proceedings of NAACL, 2019.