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The code is moved to the repo at https://github.com/INK-USC/procedural-extraction. Sorry for the inconvenience.

Procedural Extraction

Code for paper Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis, in proceedings of ACL 2019

This code provides a framwork for extracting procedural information from documents. Please refer to our ACL paper (arXiv) for further descriptions.

Quick Links

Install

  1. Run install script to download word embeddings, install pre-trained models
     bash install.sh
  2. Start StandfordCoreNLP Server (please refer to here if your are not familiar with StandfordCoreNLP). This server is required for tokenization in preprocessing and pattern-based extraction.
    java -mx20g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer --port 9000
    You should customize the StandfordCoreNLP Server URL in pattern_extraction/corenlp.py by modifying
    nlp_server = StanfordCoreNLP('http://YOUR_SERVER:YOUR_PORT')

Run Relation Classfication

Step-by-step commands for running experiment on Relation Classification with setting:

  • Model = Mask_max
  • Fuzzy-matching Method = Glove 300d
  • Context-level K = 2
  • Sampling Portion = 4:2:1

Please refer to the ArgumentParser in code for more details.

Preprocessed Dataset

Preprocessed Relation Classification Dataset is available in preprocessed/relation.tar.gz.

Drop the extracted relation folder into dataset folder. Then you can run the Model of Relation Classfication.

Preprocessing

Preprocessed dataset is available in the Preprocessed Dataset Section. ...or you can do preprocessing on your own by running following commands...

  1. Run preprocessing script. The example script is parsing data from protocol files and doing fuzzy matching with Glove 300d embedding.
    bash script/run_matching.sh
  2. Create Relation Classification Dataset with the extracted data.
    python create_dataset.py relation --path dataset/relation/embavg-2 --dir_extracted extracted/embavg
  3. Build Manual Matching Testset, from manual matching annotation fuzzy_matching/answer.json on document 01
    # Manual Matching
    python extract_samples.py 1 manual --dir_extracted extracted/manual
    # Create Relation Classification Dataset for context level K=2
    python create_dataset.py relation --path dataset/relation/manual-2 --k_neighbour 1 --dir_extracted extracted/manual --dataset 1
    # Copy to Fuzzy Matching dataset as its manual-matching testset
    cp dataset/relation/manual-2/nonsplit.pkl dataset/relation/embavg-2/manual.pkl

Model

  1. Train Classification Model for 5 times
    bash run_relation.sh
  2. Get Averaged Result
    python script/reduce.py maskmax-k2
    cat logs/maskmax-k2-avg/metrics.json

Run Sequence Labeling

Preprocessing

You can also create sequence labeling dataset with the fuzzy-matching result:

mkdir -p dataset/seqlab/embavg
python create_dataset.py seqlabel --dir_extracted extracted/embavg --path dataset/seqlab/embavg

SeqLab dataset with IOBES format is created in folder dataset/seqlab/embavg

Model

Please refer to Standford-NER and LM-LSTM-CRF

Reported Numbers

Relation Classfication

Setting Argument Gen. Acc. Gen. F1 Manual Acc. Manual F1
BERT none 81.6 ± 1.0 70.1 ± 1.7 77.2 ± 2.7 62.2 ± 6.1
Context Pos. as Attention posattn 82.5 ± 1.5 72.2 ± 2.6 81.2 ± 4.7 72.7 ± 7.5
Context Pos. as Input Emb. segemb 82.8 ± 1.4 72.7 ± 1.9 78.8 ± 8.5 67.4 ± 8.1
Hidden States Masking (Avg) mask* 80.5 ± 2.7 69.0 ± 5.7 80.4 ± 7.1 73.4 ± 7.9
Hidden States Masking (Max) mask 82.3 ± 1.4 72.6 ± 3.0 87.6 ± 1.5 81.4 ± 2.4

*Enable Hidden States Masking with Average pooling with the commented code in models/bert_modeling_mask.py

  • Fuzzy-matching Method = Glove 300d
  • Context-level K = 2
  • Sampling Portion = 4:2:1

Data

6 documents in folder data for creating dataset

  1. *.src.txt: Copy from original source (transcript) files (seperated by \n)
  2. *.src.ref.txt: Line-by-line extracted by utils/copyLineByLine.vbs from original source files, with line-break shown in Microsoft Word
  3. *.tgt.txt: Original target (protocol) files (seperated by \n)

document 05 is not used in our experiments since the line numbers in protocol is not accurate.

Manual-matching testset is created on document 01, so a small portion of samples in the manual-matching testset would be overlapping with samples in fuzzy-matching trainset (though they are extracted by different methods and are resampled). This is a flaw of the manual testset setting.

Cite

If you find our work useful in your research, please consider citing:

@inproceedings{du2019acl,
  title={Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis},
  author={Junyi, Du and He, Jiang and Jiaming, Shen and Xiang, Ren},
  booktitle={Proceedings of ACL 2019},
  year={2019}
}

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Code for paper Eliciting Knowledge from Experts: Automatic Transcript Parsing for Cognitive Task Analysis, in proceedings of ACL 2019

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