This repository is an implementation of our paper "Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction".
You Li, Xupeng Zeng, Yixiao Zeng, and Yuming Lin. 2024. Enhanced Packed Marker with Entity Information for Aspect Sentiment Triplet Extraction. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'24), July 14–18, 2024, Washington, DC, USA. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3626772.3657734
Create the bert folder bert_models/bert-base-uncased from https://huggingface.co/google-bert/bert-base-uncased/tree/main
create result/ner/14lap
create result/sc/14lap
conda create -n EPMEI python=3.8.1
conda activate EPMEI
conda install -c conda-forge jsonnet
pip install allenlp==1.2.2
pip install allennlp-models==1.2.2
torch==1.7.1
pip install wandb
pip install tensorboardX
pip install tqdm
pip install seqeval==1.2.2
cd EPMEI ##Go to the model folder
pip install --editable ./transformers
We use data_prepro_getGraph.py to process data/ASTE-Data-V2-EMNLP2020/ , so that we get the syntactic adjacency matrix of each sentence, and we put the processed data in ASTE-Data-V2-EMNLP2020_pro
First run run_ner.py to predict the entity and use test3.py to process the prediction results into json format.
Then run run_sc.py for sentiment classification to get the final results.
You can use average.py to find the average of 5 experimental results.