Deep Relevance Ranking Using Enhanced Document-Query Interactions
This software accompanies the following paper:
R. McDonald, G. Brokos and I. Androutsopoulos, "Deep Relevance Ranking Using Enhanced Document-Query Interactions". Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), Brussels, Belgium, 2018. [PDF], [appendix]
It contains the code of the deep relevance ranking models described in the paper, which can be used to rerank the top-k documents returned by a BM25 based search engine.
Instructions
This is a Python 3.6 project.
Step 1: Install the required Python packages:
pip3 install -r requirements.txt
Step 2: Download the dataset(s) you intend to use (BioASQ and/or TREC ROBUST2004).
sh get_bioasq_data.sh
sh get_robust04_data.sh
For each dataset, the following data are provided (among other files):
- Top-k documents retrieved by a BM25 based search engine (Galago) for each query of the corresponding dataset.
- Pre-trained word embeddings
- IDF values
Note: Downloading time may vary depending on server availability.
Step 3: Navigate to a models directory to train the specific model and evaluate its performance on the test set. E.g. navigate to the PACRR (and PACRR-DRMM) model:
cd models/pacrr
Consult the README file of each model for dedicated instructions (e.g. instructions for PACRR).