Cross-Lingual Generation and Evaluation of a Wide-Coverage Lexical Semantic Resource (L18-1)
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| Challenge: | Neural word embedding models are not interpretable for humans by themselves . we present a method that assigns explicit symbolic semantic features to words . |
| Approach: | They propose a method that assigns explicit symbolic semantic features to words in an embedding model . they use a finite list of terms to make the model interpretable for humans . |
| Outcome: | The proposed method is shown to be very efficient for word embedding models . it can be applied across languages and can be used as a searchable semantic annotation . |
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