  Semantic similarity measures are an important part in Natural Language
Processing tasks. However Semantic similarity measures built for general use do
not perform well within specific domains. Therefore in this study we introduce
a domain specific semantic similarity measure that was created by the
synergistic union of word2vec, a word embedding method that is used for
semantic similarity calculation and lexicon based (lexical) semantic similarity
methods. We prove that this proposed methodology out performs word embedding
methods trained on generic corpus and methods trained on domain specific corpus
but do not use lexical semantic similarity methods to augment the results.
Further, we prove that text lemmatization can improve the performance of word
embedding methods.
