Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity
Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Shehan Perera, Vindula Jayawardana, Dimuthu Lakmal & Madhavi Perera
Journal of Information Organization, Vol. 7, No. 3, 2017.
DOI: 
Link: http://www.dline.info/jio/fulltext/v7n3/jiov7n3_2.pdf
Abstract:
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.