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.

本研究引入了一个领域特定的语义相似度方法，该方法是由 word2vec 和基于词典的语义相似度方法的协同联盟所创建的，并证明了该方法优于通用语料库和域特定语料库训练的词嵌入方法，还证明了词形归并可以提高词嵌入方法的性能。