liangkai / qb

QANTA quiz bowl system

QANTA

Setups

0. You'll need Python, R, Gorobi, Spark, and Vowpal Wabbit installed and accessible on the path.

1. Either copy non_naqt.db to data/questions.db, simlink it, or copy your own questions.db file.

2. Run the script "python util/install_python_packages.py", which will install several python packages you'll need. (You may need admin access.)

3. Run the script "python util/install_nltk_data.py", which will download some nltk data. You should not use admin access for this script.

4. Download the Illinois Wikifier code (VERSION 2). Place the data directory in data/wikifier/data and put the wikifier-3.0-jar-with-dependencies.jar in the lib directory.

http://cogcomp.cs.illinois.edu/page/software_view/Wikifier

Steps

1. Generate the Makefile

python generate_makefile.py

2. Generate the guess database (this takes a while, depends on DAN---60 hours---and guesses---40 hours)

make data/guesses.db

3. Generate the LM pickle (18 hours)

make data/lm.pkl

4. generate features, train all models, and get predictions.

make all_sentence_buzz

Feature timings:

  *  classifier: 216 features lines per sec
  *  lm: 139.028408 feature lines per sec
  *  deep: 84.391876 feature lines per sec
  *  text: 158.384899 feature lines per sec
  *  wikilinks: 62.842486 feature lines per sec
  *  answer_present: 155.469810 feature lines per sec

Steps for quick test

If you are interested in getting the qb system running end to end without training the full system, you can follow these steps.

1. Generate the Makefile like above
2. Run make data/deep/glove.840B.300d.txt.gz to download some data