Transition-based end-to-end Triplet Extraction

Source code for Neurocomputing paper: Neural Transition Model for Aspect-based Sentiment Triplet Extraction with Triplet Memory

Transition scheme

Triplet Memory

Dependencies:

• DyNET 2.1
• Pyyaml 5.1
• gensim 3.7.3
• Pytorch 1.1.0
• pytorch-transformer 1.2.0
• flair 0.4.3
• AllenNLP

Dataset:

See example data data/triplet/14lap/train.json.

Drivers updated ok but the BIOS update froze the system up and the computer shut down .####Drivers=T-POS updated=O ok=O but=O the=O BIOS=TT-NEG update=TT-NEG froze=O the=O system=TTT-NEG up=O and=O the=O computer=O shut=O down=O .=O####Drivers=O updated=O ok=S but=O the=O BIOS=O update=O froze=SS the=O system=O up=O and=O the=O computer=O shut=O down=O .=O####[([0], [2], 'POS'), ([5, 6], [7], 'NEG'), ([9], [7], 'NEG')]

Each line includes four parts which seperate by '####':

• raw input sentence.
• Aspect terms with sentiment polarity which are labeled with 'O/T/TT/TTT'.
• Opinion term which are labeled with 'O/S/SS/SSS'.
• Triplets which is formed as '[(aspect term, opinion term, polarity)]'

Configurations

• data_config.yaml (for locating file paths)
• joint_config.yaml (for parameters tuning)

Preprocess for preparing ready-to-go data:

• Put glove.6B.100d.txt in embedding_dir which is set in the data_config.yaml

• Then make vocabulary and pickle instances by (Note: we employ Corenlp to obtain the dependency tree and POS tags):

python preprocess.py

• (Optional) Generate BERT Embeddings (bert-base-uncased):

python gen_bert_emb.py

Note that if you don`t use BERT, set use_sentence_vec to false in joint_config.yaml.

Train & Evaluate:

python train.py