Code for SemEval-2018 Task 10: Capturing Discriminative Attributes
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This is Luminoso's entry to SemEval-2018 task 10, "Capturing Discriminative Attributes".

It uses information from ConceptNet, WordNet, Wikipedia, and Google Ngrams as inputs to a simple linear classifier.

This code corresponds to run 3, a late entry to fix a show-stopping bug in producing the test results. Run 3 achieved a test F-score of 73.68%, and can be found as our entry on the post-evaluation leaderboard on CodaLab. The confidence interval of this score overlaps with the high score of 75%.

Input data

The input data is available on Zenodo. Download the Zip file and extract it into discriminatt/more-data.

Reproducing results

To reproduce this result:

  • Activate a Python 3 environment where you can install packages

  • Install ConceptNet 5.5. Be warned that this comes with a number of setup steps of its own. You won't need strictly need the database, but you will at least need its data/db/wiktionary.db file, for lemmatizing words.

  • Run python develop

  • Make sure you have the input data in discriminatt/more-data, as described above

  • Run python discriminatt/

The output results come from the full classifier, followed by "ablated" versions of the classifier with features disabled, followed by a simple one-feature heuristic described in our paper.