ScholarWorks@숙명여자대학교

초록

With the massive explosion of mosquito data, dealing with large amounts of text data has become problematic. Although more and more complex methods can understand and process semantics more accurately, they become less applicable to large-scale text data. Text hashing is one of the effective approaches, since it may reduce processing speed to a completely different scale extreme. Previous study has focused on single document classification, or the document pair binary classification problem. In this paper, we propose a supervised multi conditional semantic text hashing method. Experimental results on public datasets show that our method can generate multi conditional hash code.