Negation Cue Detection

Requirements

Python 3.5+ (recommended: Python 3.6)
Python packages: list of packages are provided in env-setup/requirements.txt file.
Embedding: Download the file "glove.6B.300d.txt" from https://nlp.stanford.edu/projects/glove/ and put it in ./embeddings/globe directory

# Create virtual env (Assuming you have Python 3.5 or 3.6 installed in your machine) -> optional step
python3 -m venv your_location/negation-cue
source your_location/negation-cue/bin/activate

# Install required packages -> required step
pip install -r ./env-setup/requirements.txt
python -m spacy download en_core_web_sm
export HDF5_USE_FILE_LOCKING='FALSE'

How to Run

• Example command line to train the cue-detector:

  python train.py -c ./config/config.json 

• Arguments:
  • -c, --config_path: path to the configuration file, (required)

*Note that: Training the system is optional, a trained model is already provided in "./model" directory. The pre-trained model can be used to predict negation cues in a text file (step is given below).

• Example command line to apply prediction on a given text file.

  python predict.py -c ./config/config.json -i ./data/sample-io/input_file.txt -o ./data/sample-io/

• Arguments:
  • -c, --config-path: path to the configuration file; (required). Contains details parameter settings.
  • -i, --input-file-path: path to the sample input file (text file, one sentence per line); (required)
  • -o, --output-dir: path to the output directory (output files will be created in this directory); (required)
  • --cd_sco_eval: if true, then creates a prediction file (in "./data/cd-sco-prediction/" by default) to evaluate cd-sco test corpus, (optional)

Input and Output Files

• Sample input file:
  ./data/sample-io/input_file.txt (input file must contain one sentence per line)

• Sample output files:
  ./data/sample-io/with_neg.txt (contains sentences with negation, one sentence per line)
  ./data/sample-io/with_neg_cue_tags.txt (contains sentences (tokenized) with negation tag (Y if token is a negation cue, otherwise N)
  ./data/sample-io/without_neg.txt (contains sentences without negation, one sentence per line)

Citation

Negation cue detection is part of predicting scope of negation which is also part of the paper "Predicting the Focus of Negation: Model and Error Analysis".

@inproceedings{hossain-etal-2020-predicting,
    title = "Predicting the Focus of Negation: Model and Error Analysis",
    author = "Hossain, Md Mosharaf  and
      Hamilton, Kathleen  and
      Palmer, Alexis  and
      Blanco, Eduardo",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.acl-main.743",
    pages = "8389--8401",
    abstract = "The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.",
}