SimLex999

We present a novel word level vector rep- resentation based on symmetric patterns (SPs). For this aim we automatically ac- quire SPs (e.g., “X and Y”) from a large corpus …...

We present extensive evaluations compar- ing the performance of taxonomy-based and corpus-based approaches on SimLex- 999. The results confirm our hypothesis that taxonomy-based approaches are more suitable to identify similarity. …...

In this work, we present a novel counter-fitting method which injects antonymy and synonymy constraints into vector space representations in order to improve the vectors’ capability for judging semantic similarity. …...

AbstractWord vectors with varying dimensionalities and produced by different algorithms have been extensively used in NLP. The corpora that the algorithms are trained on can contain either natural language text …...

The paper addresses the Polish version of SimLex-999 which we extended to contain not only measurement of similarity but also relatedness. The data was translated by three independent linguists; discrepancies …...

AbstractState of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Standard tasks and datasets for intrinsic …...

AbstractIs it possible to use images to model verb semantic similarities? Starting from this core question, we developed two textual distributional semantic models and a visual one. We found particularly …...