However, a generic and flexible method for semantic matching of short texts as in (Samuel, 2016) which leveraged word embeddings of different dimensionality obtained by different algorithms (GloVe and word2vec) and from different sources where the purpose was to go from word-level to short-text level semantics by combining insights from methods based on external sources of semantic knowledge with word embeddings.
(Prazak et al, 2012) attempt to estimate the similarity score between chunks based upon estimating semantic similarity of individual words and compiling them into one number for a given chunk pair.