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초록
영어
Since text mining has been assumed to apply for unformatted text (document), it is necessary to represent text with simplified models. One of the most commonly used models is the vector space model, in which text is represented as a bag of words. Recently, many researches tried to apply a graph-based text model for representing semantic relationships between words. In this paper, we surveyed research trends of graph-based text representation models for text mining. We summarized the models, their features and forecasted further researches.
목차
Abstract
1. Introduction
2. Vector Space Model
3. Classification of Graph-based Text Model
3.1. Classification by Graph Format
3.2. Classification by Graph Contents
4. Technologies for Graph-based Text Mining
4.1. PageRank
4.2. TextRank and LexRank
4.3. HITS
4.4. PMI
4.5. Frequent Itemset Mining
5. Conclusions and Further Research Trends
Acknowledgements
References
1. Introduction
2. Vector Space Model
3. Classification of Graph-based Text Model
3.1. Classification by Graph Format
3.2. Classification by Graph Contents
4. Technologies for Graph-based Text Mining
4.1. PageRank
4.2. TextRank and LexRank
4.3. HITS
4.4. PMI
4.5. Frequent Itemset Mining
5. Conclusions and Further Research Trends
Acknowledgements
References
저자정보
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자료제공 : 네이버학술정보