A major strength of these learned word embeddings is that they are able to capture useful semantic information that can be easily used in other tasks of interest such as semantic similarity and relatedness between pair of words (Mikolov et al., 2013a;Pennington et al., 2014;Wilson and Mihalcea, 2017) and dependency parsing (Chen and Manning, 2014;Dyer et al., 2015).
these models treat names and entities no more than the tokens used to mention them.