This is the source code for a system to automatically disambiguate potentially idiomatic expressions (PIEs, for short) in text. It implements four methods of doing so: a baseline most-frequent-sense method, a baseline canonical form-based method (Fazly et al., 2009), a lexical cohesion graph-based method (Sporleder & Li, 2009), and a variation on that method using literal representations of idioms' figurative senses. It evaluates those methods on a combination of four corpora, the VNC-Tokens corpus, the IDIX corpus, the PIE Corpus, and the SemEval-2013 Task 5b dataset. For a detailed description of the systems, see our LAW-MWE-CxG paper.

To run this code, you'll need the following Python setup:

• Python 2.7.6
• beautifulsoup4 4.5.1
• numpy 1.14.0
• scipy 0.19.1
• spacy 2.0.6 + en_core_web_sm 2.0.0

Different versions might work just as well, but cannot be guaranteed.

You'll also need:

• the British National Corpus
• the GloVe embeddings
• the VNC-Tokens Dataset
• the IDIX Corpus
• the PIE Corpus
• the SemEval-2013 Task 5b Dataset

• Clone the repository
• Create subdirectories called working and ext
• Add these symlinks (or edit config.py):
  • create a symlink ext/BNC to the Texts directory of your copy of the BNC
  • create a symlink ext/glove to the directory containing the GloVe embeddings
  • create symlinks ext/VNC, ext/IDIX, ext/PIE_Corpus, and ext/SemEval to the main directory of the respective corpora
• Try and run the system with python psd.py -c 0 -m cg -gs 0s. This should run a basic lexical cohesion graph method and evaluate on the development set of the combined corpora.
• Get an overview of all options by simply running python psd.py --help

For any questions about (running) the system, feel free to contact me.