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