This repository contains the materials for the submission 'Introducing Information Retrieval for Biomedical Informatics Students' presented at the Fifth Workshop on Teaching NLP @ NAACL 2021.

• Paper - https://www.aclweb.org/anthology/2021.teachingnlp-1.16/
• Poster - https://sanyabt.github.io/files/talks/NAACL_2021.pdf

Getting started

Prerequisites

• Clone the repository
• Create a Python virtual environment or conda environment with python=3.8
• Download and unzip the fine tuned models in files clinBertFineTunedMedNLI and BertCasedFineTunedMedNLI. These are used in Notebook 3.

Install packages

• Using the requirements file available here, run the following command to install all packages and dependencies in the Python environment.

1. Default Python install

   python -m pip install -r requirements.txt

2. If creating new Python virtual environment:

• a. Install virtualenv

  python -m pip install --user virtualenv

• b. Create venv with <env_name>

  • MacOs and Linux

  python -m venv <env_name>

  • Windows

  py -m venv <env_name>

• c. Activate venv and install requirements.txt

  source <env_name>/bin/activate
  python3 -m pip install --user virtualenv

3. Conda virtual environment

   conda create -n <env_name> python=3.7
   conda activate <env_name>
   python -m pip install -r requirements.txt

• NLTK data download with interactive installer

All data required for the NLTK code will be downloaded in the notebooks. To download rest of the NLTK data (optional), run the following in a Python shell:

>>> import nltk
>>> nltk.download()

Once the NLTK Downloader window opens, select 'All packages' in the Collections tab and click Download. For more information, see nltk.org

• word2vec as a service

Notebook 2 uses word2vec service through a Docker container to create word embeddings. To set up -

1. Clone the Github repository https://github.com/vampolo/word2vec-service.git
2. Change into the word2vec-service folder
3. Run sudo docker-compose up -d

Running the notebooks

All notebooks can be executed using Jupyter notebook or JupyterLab in the Python environment with the above setup instructions.

Troubleshoot

If nltk.download() gives error "SSL: CERTIFICATE_VERIFY_FAILED", run the following commands in the Python shell:

import nltk
import ssl

try:
    _create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
    pass
else:
    ssl._create_default_https_context = _create_unverified_https_context

nltk.download()

Citation/License

@inproceedings{taneja-etal-2021-introducing,
    title = "Introducing Information Retrieval for Biomedical Informatics Students",
    author = "Taneja, Sanya  and
      Boyce, Richard  and
      Reynolds, William  and
      Newman-Griffis, Denis",
    booktitle = "Proceedings of the Fifth Workshop on Teaching NLP",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.teachingnlp-1.16",
    pages = "96--98",
    abstract = "Introducing biomedical informatics (BMI) students to natural language processing (NLP) requires balancing technical depth with practical know-how to address application-focused needs. We developed a set of three activities introducing introductory BMI students to information retrieval with NLP, covering document representation strategies and language models from TF-IDF to BERT. These activities provide students with hands-on experience targeted towards common use cases, and introduce fundamental components of NLP workflows for a wide variety of applications.",
}