MRC approach for Aspect-based Sentiment Analysis (ABSA)

Related tags

Text Data & NLP B-MRC
Overview

B-MRC

MRC approach for Aspect-based Sentiment Analysis (ABSA)

Paper: Bidirectional Machine Reading Comprehension for Aspect Sentiment Triplet Extraction

Dataset: https://github.com/xuuuluuu/SemEval-Triplet-data

Usage

  • Prepare data:
python data_process.py --data_path data/14lap --version bidirectional (unidirectional)

Arguments:
    --data_path :       Path to the dataset
    --version   :       Optional version: unidirectional (A2O) and bidirectional (A2O + O2A) 
                        (default = 'bidirectiona')
                        Choices=['uni', 'bi', 'unidirectional', 'bidirectional']

python make_data_dual --data_path data/14lap/preprocess --version bidirectional (unidirectional)

Arguments:
    --data_path :       Path to the dataset
    --version   :       Optional version: unidirectional (A2O) and bidirectional (A2O + O2A)
                        (default = 'bidirectiona')
                        Choices=['uni', 'bi', 'unidirectional', 'bidirectional']

python make_data_standard --data_path data/14lab/pair --output_path ./data/14lap/preprocess

Arguments:
    --data_path  :      Path to the dataset
    --output_path:      Path to the output data
  • Training:
python main.py \
    --version bidirectional (unidirectional) \
    --data_path ./data/14lap/preprocess/ \
    --mode train \
    --model_type bert-base-uncased \
    --epoch_num 40 \
    --batch_size 4 \
    --learning_rate 1e-3
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