Github page with supplementary information to the paper `Entity-aware Transformers for Entity Search' by Emma Gerritse, Faegheh Hasibi and Arjen de Vries, which was accepted at SIGIR 2022, and can be accessed here
Structure of this github repository is as follows: In the Runs Directory, you can find all runs in the paper, with the same name as in the paper table 2.
In the Code directory, all code is available. All models and supplementary materials can be downloaded by running
cd Code/make.sh
Note that this will download around 40 gb of data.
We recommend running this code in an virtual environment using Python 3.7 (Using newer versions leads to conflicts with Pytorch), for example by using:
python3.7 -m venv venv
source venv/bin/activate
All Python packages can be downloaded with pip install -r requirements.txt.
To rerank, call the following function in Code:
python -m pygaggle.run.evaluate_passage_ranker --split dev --method seq_class_transformer --model pathtomodel --dataset pathtodata --index-dir pathtoindex --task msmarco --output-file pathtooutput --w2v pathtowikipedia2vec --mapper path2mapper
For example:
python -m pygaggle.run.evaluate_passage_ranker --split dev --method seq_class_transformer --model ../output/monobert-large-msmarco-finetuned_acc_batch_testmodel_acc_batch_600k_64_e6 --dataset ../data/DBpedia-Entity --index-dir ../indexes/lucene-index-dbpedia_annotated_full --task msmarco --output-file ../runs/testrun.tsv --w2v ../resources/wikipedia2vec/wikipedia-20190701/wikipedia2vec_500.pkl --mapper ../mappers/wikipedia2vec-500-cased.monobert-base-cased.linear.npy
Most of the code is based on either the E-BERT or the Pygaggle repository.
To use on your own datasets, make sure to provide all documents and queries as in the pygaggle repository, but annotate the documents before the index and the queries. Annotations should come right after the mention, for example Neil Gaiman ENTITY/Neil_Gaiman novels. We used REL, but you can use entity linker as long as the part after the ENTITY preamble is a Wikipediapage.
An example of finetuning can be found in Code/retraining_dbpedia_entity_folds.py.
Everything needed to evaluate the model can be downloaded with the script in Code/make.sh
If you just want the seperate models or Lucene indexes, they can be downloaded here.
TSV of DBpedia Entity Annotated
Lucene index for DBpedia Entity
Lucene index for DBpedia Entity annotated
EMBERT finetuned on Annotated Dbpedia, all 5 folds
MonoBERT fintetuned on DBpedia, not annotated, all 5 folds
EMBERT fintetuned on MSMARCO (EMBERT (1st) in paper)
You can cite us using
@inproceedings{Gerritse:2022:Entity,
author = {Gerritse, Emma and Hasibi, Faegheh and De Vries, Arjen},
booktitle = {Proc. of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
series = {SIGIR '22},
title = {{Entity-aware Transformers for Entity Search}},
year = {2022}
}
In case anything is missing, please either make an issue or send an emal to emma.gerritse@ru.nl