Before revision:  Word embedding models overcome this problem by constructing a standardized and continuous " meaning-space " where words are assigned a location based on relations of similarity to other words based on how they are used in natural language samples.
After revision:  Word embedding models overcome this problem by constructing a standardized and continuous " meaning space " where words are assigned a location based on relations of similarity to other words based on how they are used in natural language samples.