Semantic Textual Similarity (STS) system created by the MathLingBudapest team to participate in Tasks 1 and 2 of Semeval2015
NOTE: this code and its dependency pymachine are under constant development. To run the version of the code that was used to create the MathLingBudapest team's submissions (although many bugs have been fixed since), use the following revisions:
Task 1: https://github.com/juditacs/semeval/tree/submitted
Task 2: https://github.com/juditacs/semeval/tree/15863ba5bc7f857291322c707a899c7c802a7c88
If you'd also like to reproduce the machine similarity component as it was at the time of the submission, you'll need the following revision of the pymachine repository:
https://github.com/kornai/pymachine/tree/3d936067e775fc8aa56c06388eb328ef2c6efe75
First you need to install the SciPy stack on your machine by following instructions specific to your system on this page
Then run
sudo python setup.py install
Our pipeline relies on the hunpos tool for part-of-speech tagging, which can be downloaded from this page. After compiling, place the binary hunpos-tag and the English model en_wsj.model in the directory semeval/hunpos (or change the value of hunpos_dir in the configuration file to point to a different location).
The machine similarity component also requires the 4lang module. To download and install it, follow these instructions. Then configure it by editing the configuration file configs/sts\_machine.cfg based on the instructions in the 4lang README
The STS system can be invoked from the repo's base directory using:
cat sts_test.txt | python semeval/paraphrases.py -c configs/sts.cfg > out
cat twitter_test.txt | python semeval/paraphrases.py -c configs/twitter.cfg > out
These test files follow the format of the Semeval 2015 Tasks 1 and 2, respectively.
To use the machine similarity component, run
cat sts_test.txt | python semeval/paraphrases.py -c configs/sts_machine.cfg > out
Specifying regression mode in the final_score section uses a regression (see configs/twimash.cfg).
This mode needs to know the location of the train and test files, which are specified in the regression section:
[regression]
train: data/train.data
train_labels: data/train.labels
test: data/test.data
gold: data/test.label
binary_labels: true
outfile: data/predicted.labels
Specifying a gold file is optional, the rest of the options are mandatory. If you specify a gold file, precision, recall and F-score are computed and printed to stdout.
sample uses of regression.py:
python scripts/regression.py regression_train all.model semeval_data/sts_trial/201213_all data/1213_all/ngram data/1213_all/lsa data/1213_all/machine
for f in data/2014-test/nosim_4gr_d/STS.input.*; do topic=`basename $f | cut -d'.' -f3`; echo $topic; python scripts/regression.py regression_predict all.model data/2014-test/nosim_4gr_d/STS.input.$topic.txt.out data/2014-test/lsa_sim_bp/STS.input.$topic.txt.out data/2014-test/machine_sim_nodes2/STS.input.$topic.txt.out > data/2014-test/regr/STS.input.$topic.txt.out; done
For certain scripts to work, you may want to set the environment variables SEMEVAL_DATA and STANFORD_PARSER. On nessi6, set_env_nessi6.sh does this for you.