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http://dx.doi.org/10.12673/jant.2016.20.6.617

Query Extension of Retrieve System Using Hangul Word Embedding and Apriori  

Shin, Dong-Ha (Department of Energy IT, Gachon University)
Kim, Chang-Bok (Department of Energy IT, Gachon University)
Publication Information
Journal of Advanced Navigation Technology / v.20, no.6, 2016 , pp. 617-624 More about this Journal
Abstract
The hangul word embedding should be performed certainly process for noun extraction. Otherwise, it should be trained words that are not necessary, and it can not be derived efficient embedding results. In this paper, we propose model that can retrieve more efficiently by query language expansion using hangul word embedded, apriori, and text mining. The word embedding and apriori is a step expanding query language by extracting association words according to meaning and context for query language. The hangul text mining is a step of extracting similar answer and responding to the user using noun extraction, TF-IDF, and cosine similarity. The proposed model can improve accuracy of answer by learning the answer of specific domain and expanding high correlation query language. As future research, it needs to extract more correlation query language by analysis of user queries stored in database.
Keywords
Word embedding; Word2vec; Apriori; Cosine similarity; TF-IDF;
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Times Cited By KSCI : 3  (Citation Analysis)
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