GHH at SemEval-2018 Task 10: Discovering Discriminative Attributes in Distributional Semantics

2018-06-01 · SEMEVAL 2018 6 · Mohammed Attia, Younes Samih, Manaal Faruqui, Wolfgang Maier

This paper describes our system submission to the SemEval 2018 Task 10 on Capturing Discriminative Attributes. Given two concepts and an attribute, the task is to determine whether the attribute is semantically related to one concept and not the other. In this work we assume that discriminative attributes can be detected by discovering the association (or lack of association) between a pair of words. The hypothesis we test in this contribution is whether the semantic difference between two pairs of concepts can be treated in terms of measuring the distance between words in a vector space, or can simply be obtained as a by-product of word co-occurrence counts.

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AttributeWord Embeddings

CitiusNLP at SemEval-2018 Task 10: The Use of Transparent Distributional Models and Salient Contexts to Discriminate Word Attributes

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This article describes the unsupervised strategy submitted by the CitiusNLP team to the SemEval 2018 Task 10, a task which consists of predict whether a word is a discriminative attribute between two other words. Our str…

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This paper presents the two systems submitted by the meaning space team in Task 10 of the SemEval competition 2018 entitled Capturing discriminative attributes. The systems consist of combinations of approaches exploitin…

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Relation Extraction