Capturing Discriminative Attributes

Google Group: https://groups.google.com/forum/#!forum/semeval2018-discriminativeattributes

Summary

State of art semantic models do an excellent job at detecting semantic similarity, a traditional semantic task; for example, a model will be able to tell that cappuccino, espresso and americano are similar to each other. It is obvious, however, that no model can claim to capture semantic competence if it does not, in addition to similarity, predict semantic differences between words. If you can tell that americano is similar to capuccino and espresso but you can't tell the difference between them, you don't know what americano is. As a consequence, any semantic model that is only good at similarity detection will be of limited practical use.

To fill this gap, we propose a novel task of semantic difference detection. The goal of our proposed task is to predict whether a word is a discriminative attribute between two other words. For example, given the words apple and banana, is the word red a discriminative attribute?
Semantic difference is a ternary relation between two concepts (apple, banana) and a discriminative feature (red) that characterizes the first concept but not the other. By its nature, semantic difference detection is a binary classification task: given a triple apple,banana,red, the task is to determine whether it exemplifies a semantic difference or not.

Organizers

• Denis Paperno (Lorraine Laboratory of Computer Science and its Applications (Loria, UMR 7503), National Center for Scientific Research (CNRS), France)
• Alessandro Lenci (Department of Philology, Literature, and Linguistics of the University of Pisa, Italy)
• Alicia Krebs (Textkernel BV, Amsterdam, Netherlands)