Text Classification Combined an Improved CHI and Category Relevance Factor

2012 ◽  
Vol 524-527 ◽  
pp. 3866-3869
Author(s):  
Pei Ying Zhang
Keyword(s):  
Natural Language ◽  
Text Classification ◽  
Classification System ◽  
Experimental Results ◽  
New Method ◽  
Main Concern ◽  
Important Improvement ◽  
Feature Weight ◽  
Series Of Experiments

Text classification is the task of assigning natural language textual documents to predefined categories based on their context. The main concern in this paper is to improve the accuracy of text classification system combined an improved CHI method and category relevance factor. Firstly, use an improved CHI method to select features from the raw features aim to reduce the dimensions of the features. Secondly, through the TF-CRF method to calculate the feature weight, this method mainly consider that the features have different distributions in different categories. Finally, we carried out a series of experiments compared with other methods using the F1-measure. Experimental results show that our new method makes an important improvement in all categories.

Semantic Similarity Metric and its Application in Text Classification

2012 ◽  
Vol 170-173 ◽  
pp. 3711-3714 ◽  
Author(s):  
Pei Ying Zhang
Keyword(s):  
Semantic Similarity ◽  
Text Classification ◽  
Feature Vector ◽  
Semantic Distance ◽  
Main Concern ◽  
Similarity Metric ◽  
Important Improvement ◽  
Text Feature ◽  
Series Of Experiments ◽  
Document Categorization

Text classification is the task of assigning natural language textual documents to predefined categories based on their context. The main concern is this paper is to improve the accuracy of text classification system combined an improved CHI method and semantic similarity metric. Firstly, use an improved CHI method to select features from the raw features aim to reduce the dimensions of the features. Secondly, calculates the semantic distance between text feature vector and categorization feature vector so as to determine the document categorization. Finally, we carried out a series of experiments compared with other methods using the F1-measure. Experimental results show that our new method makes an important improvement in all categories.

Hierarchical Text Classification Based on LDA and Domain Ontology

2013 ◽  
Vol 411-414 ◽  
pp. 1112-1116 ◽  
Author(s):  
Wei An ◽  
Qi Hua Liu
Keyword(s):  
Text Classification ◽  
Recall Rate ◽  
Domain Ontology ◽  
Experimental Results ◽  
New Method ◽  
Accuracy Rate ◽  
High Recall ◽  
High Recall Rate ◽  
Hierarchical Text Classification

This paper combines domain ontology and LDA model to propose a new method of hierarchical web text classification. Experimental results show that the method has good performance with high recall rate and accuracy rate.

A new method for monitoring the redox potential of fuel salt based on the deposition of 95Nb on Hastelloy C276

2021 ◽  
Vol 109 (5) ◽  
pp. 357-365
Author(s):  
Zhiqiang Cheng ◽  
Zhongqi Zhao ◽  
Junxia Geng ◽  
Xiaohe Wang ◽  
Jifeng Hu ◽  
...  
Keyword(s):  
Redox Potential ◽  
Molten Salt ◽  
Specific Activity ◽  
Chemical Reduction ◽  
Quantitative Approach ◽  
Experimental Results ◽  
New Method ◽  
Molten Salt Reactor ◽  
Fuel Salt ◽  
Well Correlation

Abstract To develop the application of 95Nb as an indicator of redox potential for fuel salt in molten salt reactor (MSR), the specific activity of 95Nb in FLiBe salt and its deposition of 95Nb on Hastelloy C276 have been studied. Experimental results indicated that the amount of 95Nb deposited on Hastelloy C276 resulted from its chemical reduction exhibited a positive correlation with the decrease of 95Nb activity in FLiBe salt and the relative deposition coefficient of 95Nb to 103Ru appeared a well correlation with 95Nb activity in FLiBe salt. Both correlations implied that the measurement of 95Nb activity deposited on Hastelloy C276 specimen might provide a quantitative approach for monitoring the redox potential of fuel salt in MSR.

scholarly journals A Novel Concept Acquisition Approach Based on Formal Contexts

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ting Qian ◽  
Ling Wei
Keyword(s):  
Data Analysis ◽  
Formal Concept Analysis ◽  
Real Life ◽  
Experimental Results ◽  
New Method ◽  
Concept Acquisition ◽  
Formal Concept ◽  
Knowledge Processing ◽  
Formal Concepts ◽  
Novel Concept

As an important tool for data analysis and knowledge processing, formal concept analysis (FCA) has been applied to many fields. In this paper, we introduce a new method to find all formal concepts based on formal contexts. The amount of intents calculation is reduced by the method. And the corresponding algorithm of our approach is proposed. The main theorems and the corresponding algorithm are examined by examples, respectively. At last, several real-life databases are analyzed to demonstrate the application of the proposed approach. Experimental results show that the proposed approach is simple and effective.

A modified Runge-Kutta method

SIMULATION ◽  
1968 ◽  
Vol 10 (5) ◽  
pp. 221-223 ◽  
Author(s):  
A.S. Chai
Keyword(s):  
Error Estimate ◽  
Linear Combination ◽  
Experimental Results ◽  
The Other ◽  
New Method ◽  
Kutta Method ◽  
Starting Values ◽  
Runge Kutta Method ◽  
Runge Kutta ◽  
A Minor

It is possible to replace k2 in a 4th-order Runge-Kutta for mula (also Nth-order 3 ≤ N ≤ 5) by a linear combination of k1 and the ki's in the last step, using the same procedure for computing the other ki's and y as in the standard R-K method. The advantages of the new method are: It re quires one less derivative evaluation, provides an error estimate at each step, gives more accurate results, and needs a minor change to switch to the RK to obtain the starting values. Experimental results are shown in verification of the for mula.

An Integer Linear Programming-Based Method for the Extraction of Ontology Alignment

2021 ◽  
Vol 16 (2) ◽  
pp. 25-44
Author(s):  
Naima El Ghandour ◽  
Moussa Benaissa ◽  
Yahia Lebbah
Keyword(s):  
Linear Programming ◽  
Semantic Web ◽  
Integer Linear Programming ◽  
New Method ◽  
Ontology Matching ◽  
Ontology Alignment ◽  
Programming Techniques ◽  
Data Heterogeneity ◽  
Series Of Experiments

The Semantic Web uses ontologies to cope with the data heterogeneity problem. However, ontologies become themselves heterogeneous; this heterogeneity may occur at the syntactic, terminological, conceptual, and semantic levels. To solve this problem, alignments between entities of ontologies must be identified. This process is called ontology matching. In this paper, the authors propose a new method to extract alignment with multiple cardinalities using integer linear programming techniques. The authors conducted a series of experiments and compared them with currently used methods. The obtained results show the efficiency of the proposed method.

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