Skip to content

ThePedestrian/IR4QA-Statistical-Mixture-Model

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Information Retrieval for Question Answering Systems using a Statistical Mixture Model

The project investigates using a Mixture Model to build an IR4QA system that recommends an answer to a natural language question from precompiled list of question-answer pairs.

Our dataset for training is Yahoo! Answers Comprehensive Questions and Answers version 1.03 which consists of more than 4 million question-answer pairs provided by Yahoo Labs on the WebScope site.

We describe the system, results, and some of the key takeaway lessons.

Files

  • paper.pdf - Results and resources to download the dataset.
  • ir4qa.ipynb - Jupyter notebook for exploration.
  • server.py - Flask server.
  • gui/ - HTML/CSS/JS that invokes RESTful APIs exposed by server.py

Demo

Sample questions to ask:

  • How do I get rid of stomach ache?
  • What's the meaning of life?
  • How to lose weight?
  • I have long thick hair. Is that pretty?
  • One side of my body freezes. What should I do?

Releases

No releases published

Packages

No packages published

Languages