Paper Title
A Method of Similarity Measure with Different Domains' Documents for Semantic Classification using Word2vec and Jaccard Index

Recently, people lifestyles have been changing due to ‘fourth industrial revolution’. For this reason, people’s jobs are rapidly changing. Many developed countries have defined national competency standard, and have been training professional technical personnel that required companies since they were students. The typical examples are the United Kingdom’s National Occupational Standards (NOS), and Germany’s ‘Dual System’. In Korea, though nation developed National Competency Standards, and job dictionary, they used the independent word, and curriculums of universities were made regardless them. Due to this reason, though the students get a job, they do not know practical business, therefore the company should train to students extra education. To reduce these costs, the connections needed with job dictionary, NCS, and curriculum. For this, we suggest matching algorithm using Word2vec and Jaccard Index to connect between different domains. We compare Doc2vec algorithm and existing documents similarity algorithm based on word2vec, and suggestion method to prove accuracy. The result was our suggestion algorithm’s accuracy was higher than existing word2vec algorithm about 17.52%, and doc2vec about 23.85%. Keywords - Word2vec, Doc2vec, Document Similarity, National Competency Standard, Jaccard Index