Directory Tree

+-- app (flask sevice)
   +-- messages (CBR/Lazy learning part)
      +-- data (Case-Base)
         +-- basic.csv
         +-- personal_info_component.csv
         +-- funeral_component.csv
         +-- relations_component.csv
      +-- resources 
         +-- feature_list.csv 
      +-- __init__.py
      +-- errors.py
      +-- funeral.py
      +-- main.py
      +-- personal_info.py
      +-- relations.py
      +-- basic.py
      +-- README.md
   +-- templates (HTML templates)
         +-- index.html
   +-- __init__.py
   +-- routes.py (flask routes)

+-- labelling (data labelling framewrok)
   +-- data
      +-- basic.csv
      +-- personal_info_component.csv
      +-- funeral_component.csv
      +-- relations_component.csv
   +-- resources
      +-- tagged.json1
      +-- xml-tagged.xml
      +-- xml-tagged-comps.xml
   +-- create_csv.py
   +-- create_csv_comps.py
   +-- create_xml.py
   +-- README.md

+-- reports (technical reports)
   +-- images
      +-- basic.png (example output from basic-retrieval method).
      +-- comps.png (example output from component-retrieval method).
      +-- input-form.png (screen capture of input form).
   +-- flask-app-report.pdf.
   +-- ICCBR-accepted-paper.pdf.

+-- migrations (database: useless)

+-- cbrobit.py (main flask file)
+-- LICENSE
+-- README.md

Note: Files other than listed here might not be useful.

Instructions to run

• First, install the required libraries:

  • pip install -r requirements.txt

• Run on Unix Bash (Linux, Mac):

  • export FLASK_ENV="development"
  • export FLASK_APP="cbrobit.py"
  • flask run

• Run on Windows Powershell:

  • $env:FLASK_ENV="development"
  • $env:FLASK_APP="cbrobit.py"
  • flask run

• Run on Windows CMD:

  • set FLASK_ENV="development"
  • set FLASK_APP="cbrobit.py"
  • flask run

Input-Output system

• On https://localhost:5000 there will be a form for different feature values. A screen capture is shown here:
• After submitting the form you will get a json file with a dictionary of features given as input and four possible obituaries.
• An example of obituaries generated from basic-retrieval method.
• An example of obituaries generated from component-retrieval method.

Technical Reports

• A technical report about Flask-app can be found here.
• The accepted paper at ICCBR on the experiments performed can be found here.

TODO

• Similarity measure in component retrieval. Done
• Edge cases:
  • Morphology: Things like appropriate use of 'he' or 'she'. Done
    • Possible solution: Adding the rule-based error handling. Done
  • Orthology: Things like one full stops only (mostly occurs in component-retrieval).
    • Possible solution: Deep learning based solution adaptaion.
  • Modify the feature list to handle edge-cases with a special care for relations section.
• Add the framework for data labelling. Done
  • First manual labelling in Doccano.
  • Second, conversion of Doccano's format into XML.
  • Third, conversion of XML into tabular form, i.e., CSV.
• Add a list of features categorised in component type. Done
• Remove the tags from generated text which are not given in input. Done
• Maybe break the obituary into two sections, 'funeral' and 'non-funeral', for sending it back.
• Try to add message about earlier life.
  • Possible Solution: Collect more data reflecting messages on earlier life.

Longer Goal

• Add more data.
• Add Active Learning for data labelling.

Citation

@inproceedings{upadhyay2020case,
  title={Case-Based Approach to Automated Natural Language Generation for Obituaries},
  author={Upadhyay, Ashish and Massie, Stewart and Clogher, Sean},
  booktitle={International Conference on Case-Based Reasoning},
  pages={279--294},
  year={2020},
  organization={Springer}
}