Machine Learning

Dependency Cluster Attention for Aspect-level sentiment classification

DLCF-DCA

codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification. submitted to 《Neurocomputing》.

Requirement

  • Python >= 3.6
  • PyTorch >= 1.0
  • pytorch-transformers == 1.2.0
  • SpaCy >= 2.2

To use our models, you need download en_core_web_sm by python -m spacy download en_core_web_sm

Training

python train.py --model dlcf_dca

Model Architecture

dlcf_dca

Note

Some important scripts to note:

  • datasets/semeval14/*.seg: Preprocessed training and testing sentences in SemEval2014.
  • datasets/semeval15/*.seg: Preprocessed training and testing sentences in SemEval2015.
  • datasets/semeval16/*.seg: Preprocessed training and testing sentences in SemEval2016.
  • models/dlcf_dca.py: the source code of DLCF_DCA model.
  • data_utils.py/ABSADataSet class: preprocess the tokens and calculates the shortest distance to target words and cluster via the Dependency Syntax Parsing Tree.

Out of Memory

Since BERT

 

 

 

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