Codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification. submitted to 《Neurocomputing》.
We exploit a efficient and easy-to-use aspect-based sentiment analysis framework PyABSA. Futhermore, we integrate the optimized DLCF-DCA model into this framework.
You can easily train our DLCF-DCA models and design your models based on PyABSA.
To use PyABSA, install the latest version from pip or source code:
pip install pyabsa==1.1.24
我们开发了一个高效易用的方面级情感分析框架PyABSA，并将优化后的DLCF-DCA模型整合到这个框架之中。
您可以基于PyABSA快速地开始训练DLCF-DCA模型并设计您自己的模型。
您可以通过以下代码来安装PyABSA ：
pip install pyabsa==1.1.24
- Python >= 3.6
- PyTorch >= 1.0
- transformers >= 2.4.0
- SpaCy >= 2.2
To use our models, you need download en_core_web_sm by
python -m spacy download en_core_web_sm
Some important scripts to note:
- dlcf_dca_bert.py: the source code of DLCF_DCA model.
- apc_utils_for_dlcf_dca.py: preprocess the tokens and calculates the shortest distance to target words and cluster via the Dependency Syntax Parsing Tree.
- apc_utils.py: calculates the SynRD from aspect term to target words via the Dependency Syntax Parsing Tree.
- apc_trainer.py: training process instruction.
Our code will automatically download the datasets in intergrated_datasets folder
- integrated_datasets/apc_datasets/SemEval/laptop14/*.seg: Preprocessed training and testing sentences in SemEval-2014 laptop dataset.
- integrated_datasets/apc_datasets/SemEval/restaurant14/*.seg: Preprocessed training and testing sentences in SemEval-2014 restaurant dataset.
- integrated_datasets/apc_datasets/SemEval/restaurant15/*.seg: Preprocessed training and testing sentences in SemEval-2015 restaurant dataset.
- integrated_datasets/apc_datasets/SemEval/restaurant16/*.seg: Preprocessed training and testing sentences in SemEval-2016 restaurant dataset.
- integrated_datasets/apc_datasets/TShirt/*.seg: Preprocessed training and testing sentences in Tshirt dataset.
- integrated_datasets/apc_datasets/Television/*.seg: Preprocessed training and testing sentences in Television dataset.
from pyabsa.functional import Trainer
from pyabsa.functional import APCConfigManager
from pyabsa.functional import ABSADatasetList
from pyabsa.functional import APCModelList
apc_config_english = APCConfigManager.get_apc_config_english()
apc_config_english.model = APCModelList.DLCF_DCA_BERT
apc_config_english.lcf = "cdm" # or "cdw"
apc_config_english.dlcf_a = 2
apc_config_english.dca_p = 1
apc_config_english.dca_layer = 3
apc_config_english.dropout = 0.5
apc_config_english.num_epoch = 10
apc_config_english.l2reg = 0.00001
apc_config_english.seed = {0, 1, 2, 3}
apc_config_english.evaluate_begin = 0
dataset_path = ABSADatasetList.Restaurant14
sent_classifier = Trainer(config=apc_config_english,
dataset=dataset_path, # train set and test set will be automatically detected
checkpoint_save_mode=1, # =None to avoid save model
auto_device=True # automatic choose CUDA or CPU
)
We share some checkpoints for the DLCF-DCA models in Google drive.
Our codes will automatically download the checkpoint.
| checkpoint name | Laptop14 (acc) | Laptop14 (f1) |
|---|---|---|
| 'dlcf-dca-bert1' | 81.50 | 78.03 |
| checkpoint name | Restaurant14 (acc) | Restaurant14 (f1) |
|---|---|---|
| 'dlcf-dca-bert2' | 86.79 | 80.53 |
import os
from pyabsa import APCCheckpointManager, ABSADatasetList
os.environ['PYTHONIOENCODING'] = 'UTF8'
sentiment_map = {0: 'Negative', 1: 'Neutral', 2: 'Positive', -999: ''}
sent_classifier = APCCheckpointManager.get_sentiment_classifier(checkpoint='dlcf-dca-bert1', #or 'dlcf-dca-bert2'
auto_device='cuda', # Use CUDA if available
sentiment_map=sentiment_map
)
# batch inferring_tutorials returns the results, save the result if necessary using save_result=True
inference_sets = ABSADatasetList.Laptop14
results = sent_classifier.batch_infer(target_file=inference_sets,
print_result=True,
save_result=True,
ignore_error=True,
)
Apple is unmatched in product quality , aesthetics , craftmanship , and customer service .
product quality --> Positive Real: Positive (Correct)
Apple is unmatched in product quality , aesthetics , craftmanship , and customer service .
aesthetics --> Positive Real: Positive (Correct)
Apple is unmatched in product quality , aesthetics , craftmanship , and customer service .
craftmanship --> Positive Real: Positive (Correct)
Apple is unmatched in product quality , aesthetics , craftmanship , and customer service .
customer service --> Positive Real: Positive (Correct)
It is a great size and amazing windows 8 included !
windows 8 --> Positive Real: Positive (Correct)
I do not like too much Windows 8 .
Windows 8 --> Negative Real: Negative (Correct)
Took a long time trying to decide between one with retina display and one without .
retina display --> Neutral Real: Neutral (Correct)
I was also informed that the components of the Mac Book were dirty .
components --> Negative Real: Negative (Correct)
the hardware problems have been so bad , i ca n't wait till it completely dies in 3 years , TOPS !
hardware --> Negative Real: Negative (Correct)
It 's so nice that the battery last so long and that this machine has the snow lion !
battery --> Positive Real: Positive (Correct)
It 's so nice that the battery last so long and that this machine has the snow lion !
snow lion --> Positive Real: Positive (Correct)
from pyabsa.functional import APCCheckpointManager
from pyabsa.functional import Trainer
from pyabsa.functional import APCConfigManager
from pyabsa.functional import ABSADatasetList
from pyabsa.functional import APCModelList
apc_config_english = APCConfigManager.get_apc_config_english()
apc_config_english.model = APCModelList.DLCF_DCA_BERT
apc_config_english.lcf = "cdw" # or "cdm"
apc_config_english.dlcf_a = 2
apc_config_english.dca_p = 1
apc_config_english.dca_layer = 3
apc_config_english.max_seq_len = 80
apc_config_english.dropout = 0.5
apc_config_english.num_epoch = 10
apc_config_english.l2reg = 0.00001
apc_config_english.seed = {0, 1, 2, 3}
apc_config_english.evaluate_begin = 0
checkpoint_path = APCCheckpointManager.get_checkpoint('dlcf-dca-bert1')
Laptop14 = ABSADatasetList.Laptop14
sent_classifier = Trainer(config=apc_config_english,
dataset=Laptop14,
from_checkpoint=checkpoint_path,
checkpoint_save_mode=1,
auto_device=True
)
We have based our model development on PyABSA. Thanks for their contribution.