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sebastianruder / NLP-progress

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

Home Page:https://nlpprogress.com/

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New task: Capturing discriminative attributes (SemEval 2018 Task 10)

Yermouth opened this issue · comments

commented

This is a new SemEval 2018 binary classification task (task 10) where participants were asked to identify whether an attribute could help discriminate between two concepts. Unlike other word similarity prediction tasks, this task focuses on the semantic differences between words.

e.g. red(attribute) can be used to discriminate apple (concept1) from banana (concept2) -> label 1

More examples:
Attribute, concept1, concept2, label
bookcase, fridge, wood, 1
bucket, mug, round, 0
angle, curve, sharp, 1
pelican, turtle, water, 0
wire, coil, metal, 0

Reference:
https://www.aclweb.org/anthology/S18-1117

Question:

  • Where should I add this?
  • Under an existing category? If yes, which one?

Personally, I think this task is similar to Relation prediction, but it has three arguments (one attribute and two concepts) instead of two.

commented

Hi Sunny, this is really cool! Thanks for raising this! Yes, I think it's fine to add this to relation prediction for now. We might shuffle around things later. :)

commented

PR #343 has been merged, so can be closed.