The great majority of composi- tional models in distributional semantics present methods to compose distributional vectors or tensors in a representation of the sentence. Here we propose to enrich the best performing method (vector addition, which we take as a baseline) with distri- butional knowledge about events, outper- forming our baseline.
MEDEA: Merging Event knowledge and Distributional vEctor Addition
Ludovica PannittoPrimo
;Alessandro LenciUltimo
2018-01-01
Abstract
The great majority of composi- tional models in distributional semantics present methods to compose distributional vectors or tensors in a representation of the sentence. Here we propose to enrich the best performing method (vector addition, which we take as a baseline) with distri- butional knowledge about events, outper- forming our baseline.File in questo prodotto:
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