Term Similarity and Weighting Framework for Text Representation

2011 ◽  
pp. 304-318 ◽  
Author(s):  
Sadiq Sani ◽  
Nirmalie Wiratunga ◽  
Stewart Massie ◽  
Robert Lothian
Keyword(s):  
Text Representation ◽  
Term Similarity

Integration of Inference-Validating Knowledge and Text Representation

1992 ◽  
Author(s):  
Michael Halldorson ◽  
Murray Singer
Keyword(s):  
Text Representation

scholarly journals Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

2021 ◽  
Author(s):  
Bo Wang ◽  
Tao Shen ◽  
Guodong Long ◽  
Tianyi Zhou ◽  
Ying Wang ◽  
...  
Keyword(s):  
Representation Learning ◽  
Knowledge Graph ◽  
Text Representation

scholarly journals Cancer Research Trend Analysis Based on Fusion Feature Representation

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 338
Author(s):  
Jingqiao Wu ◽  
Xiaoyue Feng ◽  
Renchu Guan ◽  
Yanchun Liang
Keyword(s):  
Cancer Research ◽  
Trend Analysis ◽  
Research Trends ◽  
Feature Representation ◽  
Research Trend ◽  
Good Representation ◽  
Text Representation ◽  
Classical Text ◽  
Representation Model ◽  
Text Feature

Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word representation. A good representation can capture semantic and structural information. In this paper, two fusion algorithms are proposed, namely, the Tr-W2v and Ti-W2v algorithms. They are based on the classical text feature representation model and consider the importance of words. The results show that the effectiveness of the two fusion text representation models is better than the classical text representation model, and the results based on the Tr-W2v algorithm are the best. Furthermore, based on the Tr-W2v algorithm, trend analyses of cancer research are conducted, including correlation analysis, keyword trend analysis, and improved keyword trend analysis. The discovery of the research trends and the evolution of hotspots for cancers can help doctors and biological researchers collect information and provide guidance for further research.

Design of Text Categorization System Based on SVM

2012 ◽  
Vol 532-533 ◽  
pp. 1191-1195 ◽  
Author(s):  
Zhen Yan Liu ◽  
Wei Ping Wang ◽  
Yong Wang
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Extraction Methods ◽  
Support Vector ◽  
Text Representation ◽  
Text Feature ◽  
Categorization System ◽  
Classifier Training

This paper introduces the design of a text categorization system based on Support Vector Machine (SVM). It analyzes the high dimensional characteristic of text data, the reason why SVM is suitable for text categorization. According to system data flow this system is constructed. This system consists of three subsystems which are text representation, classifier training and text classification. The core of this system is the classifier training, but text representation directly influences the currency of classifier and the performance of the system. Text feature vector space can be built by different kinds of feature selection and feature extraction methods. No research can indicate which one is the best method, so many feature selection and feature extraction methods are all developed in this system. For a specific classification task every feature selection method and every feature extraction method will be tested, and then a set of the best methods will be adopted.

Repetition Effects From Paraphrased Text: Evidence for an Integrated Representation Model of Text Representation

2000 ◽  
Vol 29 (1) ◽  
pp. 61-81 ◽  
Author(s):  
Gary E. Raney ◽  
David J. Therriault ◽  
Scott R. B. Minkoff
Keyword(s):  
Text Representation ◽  
Repetition Effects ◽  
Representation Model
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