parmesan_benchmarks

This is the implementation of the experimental procedures and corpus generation methods described in our 2024 LREC-COLING Paper Parmesan: Annotated Mathematical Corpora.

Because of conflicting dependencies, we recommend using separate Python virtual environments for corpus generation and each of the different terminology and definition extraction models.

Corpus Generation

Install the requirements for corpus generation and download the base corpora:

pip install -r requirements/corpora.txt
wget -P corpora/json https://raw.githubusercontent.com/ToposInstitute/tac-corpus/main/tac_metadata.json
git clone https://github.com/ncatlab/nlab-content-html.git corpora/html/nlab-content-html
git clone https://github.com/ToposInstitute/CT-corpus corpora/latex/bct-corpus

The BCT corpus needs to be converted from LaTeX into HTML. This is done with latexml, which must be installed on your system following the directions on the website.

latexmlpost --dest=corpora/html/bct-corpus/BasicCategoryTheory.html corpora/latex/bct-corpus/latex/BasicCategoryTheory.xml

Some errors and warnings will appear, but these can generally be ignored.

Finally, build the conll-u corpora from the original sources:

python scripts/build_conllu.py

Benchmark Generation

Most benchmarks are generated from the corpora and associated metadata. Most benchmarks are generated automatically with the following command:

python scripts/build_benchmarks.py

The MWE-based benchmarks are constructed using the MWEToolkit, which must first be installed:

git clone git@gitlab.com:mwetoolkit/mwetoolkit3.git
cd mwetoolkit3
make
cd ..

Then, run the toolkit on each of the three corpora:

python mwetoolkit3/bin/candidates.py -p patterns/np.xml --to PlainCandidates corpora/conllu/bct.conll > benchmarks/bct/mwe.txt
python mwetoolkit3/bin/candidates.py -p patterns/np.xml --to PlainCandidates corpora/conllu/tac.conll > benchmarks/tac/mwe.txt
python mwetoolkit3/bin/candidates.py -p patterns/np.xml --to PlainCandidates corpora/conllu/nlab.conll > benchmarks/nlab/mwe.txt

Finally, we need to construct extractive versions of the base benchmarks:

python scripts/build_extractive_benchmarks.py

Some of the models require additional corpus processing.

DyGIE++

To convert the corpora into an appropriate format for DyGIE++, run the following script:

python scripts/build_dygiepp.py

Terminology Generation

DyGIE++

Note that you will probably need to switch to a new virtual environment for DyGIE++, since it is incompatible dependencies. First, install DyGIE++ and its dependencies as well as the pretrained model:

git clone https://github.com/dwadden/dygiepp.git
pip install -r requirements/dygiepp.txt
mkdir dygiepp/pretrained
wget -P dygiepp/pretrained https://s3-us-west-2.amazonaws.com/ai2-s2-research/dygiepp/master/scierc.tar.gz

To generate the analysis for each corpus, run:

cd dygiepp
allennlp predict pretrained/scierc.tar.gz \
    ../corpora/dygiepp/bct.jsonl \
    --predictor dygie \
    --include-package dygie \
    --use-dataset-reader \
    --output-file ../predictions/dygiepp/bct.jsonl \
    --cuda-device 0 \
    --silent

Textrank

The textrank dependencies are installed with the corpus dependencies above, as there are no conflicting dependencies at this time. To generate the terminology, run:

python scripts/build_textrank.py

SpERT.PL

Again, a separate virtual environment is recommended. Download the repository and extract the code:

git clone https://github.com/dksanyal/SpERT.PL
unzip SpERT.PL/CodeFinal.zip -d SpERT.PL/

Download the pretrained models:

wget -P SpERT.PL/CodeFinal/InputsAndOutputs/pretrained/ https://s3-us-west-2.amazonaws.com/ai2-s2-research/scibert/huggingface_pytorch/scibert_scivocab_cased.tar
tar xvf SpERT.PL/CodeFinal/InputsAndOutputs/pretrained/scibert_scivocab/cased/scibert_scivocab_cased.tar

Install the required dependencies:

pip install -r requirements/spert.txt

Train the model:

python scripts/train_spert.py

Run the model to produce predictions (ignore the output scores, we are only generating examples, not evaluating them yet):

python scripts/run_spert.py

It may be necessary to change the names of the directories in these scripts to match the latest run.

Copy the predictions to the appropriate directory (you will need to change the directory name to match yours):

cp SpERT.PL/CodeFinal/InputsAndOutputs/data/log/\'scierc_eval\'/DIRNAME/examples_entities_sorted_test_epoch_0.html predictions/spert/bct.html

PL-Marker

Again, a separate virtual environment is recommended. Download the repository and install the dependencies:

git clone https://github.com/thunlp/PL-Marker
pip install -r PL-Marker/requirements.txt
pip install -e PL-Marker/transformers

Download the pretrained models from here and place them in PL-Marker/sciner-scibert.

Also download the necessary BERT models.

wget -P PL-Marker/bert_models/scibert_scivocab_uncased https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/pytorch_model.bin
wget -P PL-Marker/bert_models/scibert_scivocab_uncased https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/vocab.txt
wget -P PL-Marker/bert_models/scibert_scivocab_uncased https://huggingface.co/allenai/scibert_scivocab_uncased/resolve/main/config.json

PL-Marker uses the same data format as DyGIE++, so we can reuse the same corpora from before. To run PL-Marker on a corpus, run:

python scripts/run_acener.py --model_type bertspanmarker \
    --model_name_or_path  PL-Marker/bert_models/scibert_scivocab_uncased --do_lower_case \
    --data_dir corpora/dygiepp/ \
    --per_gpu_eval_batch_size 16 \
    --max_seq_length 512 --max_pair_length 256 --max_mention_ori_length 8 \
    --do_eval \
    --fp16 --seed 42 --onedropout --lminit \
    --test_file bct.jsonl \
    --output_dir predictions/plmarker/bct --overwrite_output_dir --output_results

Evaluation

To run evaluations on all of the predicted values for all corpora and terminology generation models, run:

python scripts/evaluate_terminology.py