And indeed, this paper will show that, depending on the choice of clustering method, semantics-based similarity measures such as summed word embeddings and deep neural networks can have an advantage over more traditional similarity metrics, such as n-gram counts, n-gram tf-idf vectors, and dependency tree kernels, when applied to creative texts.
unlike in most text similarity tasks, in clustering the choice of similarity metric interacts with both the choice of clustering method and the properties of the text.