- 期刊
A Novel Vector Representation Model for Text Mining Based on Enhancing Features
並列摘要
Semantics relationships such as polysemy and synonymy etc. contained in text documents can be used to enhance the features in the vector representation. Previous feature representation methods rarely consider such semantic relations to improve mining precision, because taking semantic relations into account greatly depends on the learning process. In this paper, we present a novel document representation method which utilizes semantic relations to enhance the features. To obtain semantic relations, we use the external resource of Wikipedia. Moreover, a term-concept mapping structure is constructed to describe features. When concepts are extracted from document, Markov random walk model is used to handle the semantic relationships between the concepts and related concepts are voted to enhance the features. We conduct comprehensive experiment using 20-newsgroup corpus and Reuters corpus. Results show that the proposed method has good performance in text mining.
延伸閱讀
- 許立憲（2016）。Domain-Based Text Mining System for Detecting Topics and Recognizing Customer Requirements〔碩士論文，國立暨南國際大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0020-2508201614212100
- Huang, B., & Yu, G. (2014). Research and Application of Personalized Modeling Based on Individual Interest in Mining. Abstract and Applied Analysis, 2014(), 975-982-795. https://doi.org/10.1155/2014/514295
- 林昆賢、蔡俊明（2019）。A Study on Pre-trained Word2Vec Model in Deep Learning Based Natural Language Processing。國教新知，66(1)，15-31。https://doi.org/10.6701/TEEJ.201906_66(1).0002
- Hsaio, W. H. (2015). Text Mining with Semi-Supervised Learning [doctoral dissertation, National Chiao Tung University]. Airiti Library. https://doi.org/10.6842/NCTU.2015.00317
- 林柏宇（2009）。Using Text Mining Technologies for a Role-Based Stock Prediction Model〔碩士論文，中原大學〕。華藝線上圖書館。https://doi.org/10.6840/CYCU.2009.00798