Papers with capturing and quantifying semantic associations
Indra: A Word Embedding and Semantic Relatedness Server (L18-1)
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Juliano Efson Sales, Leonardo Souza, Siamak Barzegar, Brian Davis, André Freitas, Siegfried Handschuh
| Challenge: | Word embedding/distributional semantic models are a fundamental component in many natural language processing (NLP) architectures. |
| Approach: | They propose a multi-lingual word embedding/distributional semantics framework which supports creation, use and evaluation of word embedded models. |
| Outcome: | The proposed tool supports the creation, use and evaluation of word embedding models. |