AddMoreClusters

Shared repository with code from paper "Capturing Evolution in Word Usage: Just Add More Clusters?" presented at WWW2020 Temporal Web workshop.

Installation, documentation

Published results were produced in Python 3 programming environment. Instructions for installation assume the usage of PyPI package manager.
To get the source code, clone the project from the repository with 'git clone https://github.com/smontariol/AddMoreClusters/'
Install dependencies if needed: pip install -r requirements.txt

To reproduce the results on the COHA corpus published in the paper run the code in the command line using following commands:

To fine-tune custom BERT model on the COHA corpus:

python3 BERT_finetuning.py --train_data_file PATH_TO_COHA_CORPUS --output_dir PATH_TO_SAVED_MODEL --model_type bert --mlm --do_train --num_train_epochs 5 --per_gpu_train_batch_size 8 --model_name_or_path bert-base-uncased

Script src/main.py generates BERT contextual embeddings, clusters them and detect semantic drift for each target word given the following conditions:

• COHA corpus is in src/corpora/COHA/text
• Gulordava and Baroni dataset is in src/Gulordava_word_meaning_change_evaluation_dataset.csv
• The path to BERT model is pretrained_models/bert-base-uncased.tar.gz

Note!

The results reported in the paper for the Affinity Propagation are reproducible with Scikit version 0.21. The Scikit-learn implementation of Affinity Propagation behaves different from the version we used (ver 0.21) to subsequent versions (ver 0.22 and higher): some bigger datasets cannot be clustered. To replicate the Affinity Propagation results, we recommend downgrading to Scikit to 0.21, as it is specified in requirements.txt

Acknowledgements

This work has been partly supported by the European Union’s Horizon 2020 research and innovation programme under grant 770299 (NewsEye) and 825153 (EMBEDDIA).