This repository contains reproducing code for the results in Word Sense Induction with Neural biLM and Symmetric Patterns paper
The code is written in python 3.6 and runs the SemEval 2013 task 13 evaluation code provided by the SemEval 2013 workshop, which is written in Java and therefore require installed JRE.
It also requires the AllenNLP python package as well as a few other packages. Below are detailed instructions for running the code.
Prerequisites:
- Java >=1.6 installed and in PATH
- Python 3.6, preferably a clean Conda environment
Installing a clean conda environment from scratch:
# create and activate new python 3.6 conda envrionment
conda create -n spwsi python=3.6 anaconda
source activate spwsi
# for cuda support install pytorch first (see link below and note the cuda version)
conda install pytorch cuda80 -c pytorch
# additional python pacakges from requirements.txt
pip install -r requirements.txt
# install spacy English model, download ELMo's output matrix and SemEval 2013 task code
python -m spacy download en
sh download_resources.shfor pytorch installation w/ cuda support see instructions at pytorch website
finally, to do the actual WSI using ELMo's LM, run spwsi_elmo.py
script arguments:
python spwsi_elmo.py -h
usage: spwsi_elmo.py [-h] [--n-clusters N_CLUSTERS]
[--n-representatives N_REPRESENT]
[--n-samples-side N_SAMPLES_SIDE] [--cuda CUDA]
[--debug-dir DEBUG_DIR] [--disable-lemmatization]
[--disable-symmetric-patterns] [--disable-tfidf]
[--append-results-file APPEND_RESULTS_FILE]
[--run-prefix RUN_PREFIX]
[--elmo-batch-size ELMO_BATCH_SIZE]
[--prediction-cutoff PREDICTION_CUTOFF]
[--cutoff-elmo-vocab CUTOFF_ELMO_VOCAB]
BiLM Symmetric Patterns WSI Demo
optional arguments:
-h, --help show this help message and exit
--n-clusters N_CLUSTERS
number of clusters per instance
--n-representatives N_REPRESENT
number of representations per sentence
--n-samples-side N_SAMPLES_SIDE
number of samples per representations side
--cuda CUDA cuda device for ELMo (-1 to disable)
--debug-dir DEBUG_DIR
logs and keys are written will be written to this dir
--disable-lemmatization
disable ELMO prediction lemmatization
--disable-symmetric-patterns
disable "x and y" symmetric pattern and predict
substitutes inplace
--disable-tfidf disable tfidf transformer
--append-results-file APPEND_RESULTS_FILE
append final run results to this file
--run-prefix RUN_PREFIX
will be prepended to log file names
--elmo-batch-size ELMO_BATCH_SIZE
ELMo prediction batch size
--prediction-cutoff PREDICTION_CUTOFF
ELMo predicted distribution top K cutoff
--cutoff-elmo-vocab CUTOFF_ELMO_VOCAB
optimization: only use top K words for faster output
matrix multiplicationto run on cuda device 0
python spwsi_elmo.py --cuda 0a detailed report will be available inside "debug" directory after running the script together with the produced task key file. you can re-run the key file scoring using evaluate_key.sh