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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Representing Syntax and Composition with Geometric Transformations ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bertolini, Lorenzo; Peng, Qiwei. - : Underline Science Inc., 2021 BASE Show details 2 Representing Syntax and Composition with Geometric Transformations ... Bertolini, Lorenzo; Weeds, Julie; Weir, David. - : arXiv, 2021 BASE Show details 3 Representing syntax and composition with geometric transformations Bertolini, Lorenzo; Peng, Qiwei; Weir, David. - : Association for Computational Linguistics, 2021 BASE Show details 4 Data augmentation for hypernymy detection Kober, Thomas; David, Weir; Bertolini, Lorenzo; Weeds, Julie. - : Association for Computational Linguistics, 2021 Abstract: The automatic detection of hypernymy relationships represents a challenging problem in NLP. The successful application of state-of-the-art supervised approaches using distributed representations has generally been impeded by the limited availability of high quality training data. We have developed two novel data augmentation techniques which generate new training examples from existing ones. First, we combine the linguistic principles of hypernym transitivity and intersective modifier-noun composition to generate additional pairs of vectors, such as “small dog - dog” or “small dog - animal”, for which a hypernymy relationship can be assumed. Second, we use generative adversarial networks (GANs) to generate pairs of vectors for which the hypernymy relation can also be assumed. We furthermore present two complementary strategies for extending an existing dataset by leveraging linguistic resources such as WordNet. Using an evaluation across 3 different datasets for hypernymy detection and 2 different vector spaces, we demonstrate that both of the proposed automatic data augmentation and dataset extension strategies substantially improve classifier performance. URL: https://www.aclweb.org/anthology/2021.eacl-main.89 http://sro.sussex.ac.uk/id/eprint/96884/ http://sro.sussex.ac.uk/id/eprint/96884/1/2005.01854.pdf BASE Hide details 5 Data Augmentation for Hypernymy Detection ... Kober, Thomas; Weeds, Julie; Bertolini, Lorenzo. - : arXiv, 2020 BASE Show details

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