The Application of Semantic Similarity in Text Classification

2013 ◽  
Vol 346 ◽  
pp. 141-144
Author(s):  
Pei Ying Zhang
Keyword(s):  
Semantic Similarity ◽  
Text Classification ◽  
Learning Algorithms ◽  
Feature Space ◽  
Curse Of Dimensionality ◽  
High Dimensionality ◽  
Challenging Problem ◽  
Weighting Method ◽  
Important Improvement

Text classification is a challenging problem which aims to automatically assign unlabeled documents to predefined one or more classes according to its contents. The major problem of text classification is the high dimensionality of the feature space. This paper proposes an approach based on the semantic similarity between the title vectors and the category vectors using the tf*rf weighting method. Experiments show that text classifier based on semantic similarity helps dimension sensitive learning algorithms such as KNN to eliminate the “curse of dimensionality” and as a result 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.

scholarly journals A Compressive Sensing Model for Speeding Up Text Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Kelin Shen ◽  
Peinan Hao ◽  
Ran Li
Keyword(s):  
Compressive Sensing ◽  
Text Classification ◽  
Feature Space ◽  
High Dimensionality ◽  
Random Projections ◽  
Text Documents ◽  
Sensing Model ◽  
Speed Up ◽  
Text Features ◽  
Time And Space Complexity

Text classification plays an important role in various applications of big data by automatically classifying massive text documents. However, high dimensionality and sparsity of text features have presented a challenge to efficient classification. In this paper, we propose a compressive sensing- (CS-) based model to speed up text classification. Using CS to reduce the size of feature space, our model has a low time and space complexity while training a text classifier, and the restricted isometry property (RIP) of CS ensures that pairwise distances between text features can be well preserved in the process of dimensionality reduction. In particular, by structural random matrices (SRMs), CS is free from computation and memory limitations in the construction of random projections. Experimental results demonstrate that CS effectively accelerates the text classification while hardly causing any accuracy loss.

scholarly journals Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: the Evaluation of Text Classification Algorithms Using Machine Learning

2019 ◽  
Author(s):  
Francesc López Seguí ◽  
Ricardo Ander Egg Aguilar ◽  
Gabriel de Maeztu ◽  
Anna García-Altés ◽  
Francesc García Cuyàs ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Text Classification ◽  
Learning Strategy ◽  
Care Service ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Face To Face ◽  
Classification Tool ◽  
The Impact

Background: the primary care service in Catalonia has operated an asynchronous teleconsulting service between GPs and patients since 2015 (eConsulta), which has generated some 500,000 messages. New developments in big data analysis tools, particularly those involving natural language, can be used to accurately and systematically evaluate the impact of the service. Objective: the study was intended to examine the predictive potential of eConsulta messages through different combinations of vector representation of text and machine learning algorithms and to evaluate their performance. Methodology: 20 machine learning algorithms (based on 5 types of algorithms and 4 text representation techniques)were trained using a sample of 3,559 messages (169,102 words) corresponding to 2,268 teleconsultations (1.57 messages per teleconsultation) in order to predict the three variables of interest (avoiding the need for a face-to-face visit, increased demand and type of use of the teleconsultation). The performance of the various combinations was measured in terms of precision, sensitivity, F-value and the ROC curve. Results: the best-trained algorithms are generally effective, proving themselves to be more robust when approximating the two binary variables "avoiding the need of a face-to-face visit" and "increased demand" (precision = 0.98 and 0.97, respectively) rather than the variable "type of query"(precision = 0.48). Conclusion: to the best of our knowledge, this study is the first to investigate a machine learning strategy for text classification using primary care teleconsultation datasets. The study illustrates the possible capacities of text analysis using artificial intelligence. The development of a robust text classification tool could be feasible by validating it with more data, making it potentially more useful for decision support for health professionals.

Research on Digital Forensics Based on Uyghur Web Text Classification

2020 ◽  
pp. 1586-1597
Author(s):  
Yasen Aizezi ◽  
Anwar Jamal ◽  
Ruxianguli Abudurexiti ◽  
Mutalipu Muming
Keyword(s):  
Mutual Information ◽  
Text Classification ◽  
Text Categorization ◽  
Digital Forensics ◽  
Feature Space ◽  
Experimental Result ◽  
Support Vector ◽  
Web Documents ◽  
Normalized Mutual Information ◽  
Plain Text

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.

Research on Digital Forensics Based on Uyghur Web Text Classification

2020 ◽  
pp. 485-496
Author(s):  
Yasen Aizezi ◽  
Anwar Jamal ◽  
Ruxianguli Abudurexiti ◽  
Mutalipu Muming
Keyword(s):  
Mutual Information ◽  
Text Classification ◽  
Text Categorization ◽  
Digital Forensics ◽  
Feature Space ◽  
Experimental Result ◽  
Support Vector ◽  
Web Documents ◽  
Normalized Mutual Information ◽  
Plain Text

This paper mainly discusses the use of mutual information (MI) and Support Vector Machines (SVMs) for Uyghur Web text classification and digital forensics process of web text categorization: automatic classification and identification, conversion and pretreatment of plain text based on encoding features of various existing Uyghur Web documents etc., introduces the pre-paratory work for Uyghur Web text encoding. Focusing on the non-Uyghur characters and stop words in the web texts filtering, we put forward a Multi-feature Space Normalized Mutual Information (M-FNMI) algorithm and replace MI between single feature and category with mutual information (MI) between input feature combination and category so as to extract more accurate feature words; finally, we classify features with support vector machine (SVM) algorithm. The experimental result shows that this scheme has a high precision of classification and can provide criterion for digital forensics with specific purpose.

scholarly journals A New Feature Selection Method for Text Classification Based on Independent Feature Space Search

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yong Liu ◽  
Shenggen Ju ◽  
Junfeng Wang ◽  
Chong Su
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Predictive Accuracy ◽  
Feature Selection Method ◽  
Feature Space ◽  
Selection Method ◽  
The Other ◽  
Feature Subset ◽  
Search Range ◽  
Text Documents

Feature selection method is designed to select the representative feature subsets from the original feature set by different evaluation of feature relevance, which focuses on reducing the dimension of the features while maintaining the predictive accuracy of a classifier. In this study, we propose a feature selection method for text classification based on independent feature space search. Firstly, a relative document-term frequency difference (RDTFD) method is proposed to divide the features in all text documents into two independent feature sets according to the features’ ability to discriminate the positive and negative samples, which has two important functions: one is to improve the high class correlation of the features and reduce the correlation between the features and the other is to reduce the search range of feature space and maintain appropriate feature redundancy. Secondly, the feature search strategy is used to search the optimal feature subset in independent feature space, which can improve the performance of text classification. Finally, we evaluate several experiments conduced on six benchmark corpora, the experimental results show the RDTFD method based on independent feature space search is more robust than the other feature selection methods.

scholarly journals Accurate prediction of DNA N4-methylcytosine sites via boost-learning various types of sequence features

BMC Genomics ◽  
2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhixun Zhao ◽  
Xiaocai Zhang ◽  
Fang Chen ◽  
Liang Fang ◽  
Jinyan Li
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Case Studies ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Feature Space ◽  
Sequence Features ◽  
Independent Test ◽  
Benchmark Datasets

Abstract Background DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristics and machine learning algorithms have been explored to identify 4mC sites from DNA sequences. However, state-of-the-art methods have limited performance because of the lack of effective sequence features and the ad hoc choice of learning algorithms to cope with this problem. This paper is aimed to propose new sequence feature space and a machine learning algorithm with feature selection scheme to address the problem. Results The feature importance score distributions in datasets of six species are firstly reported and analyzed. Then the impact of the feature selection on model performance is evaluated by independent testing on benchmark datasets, where ACC and MCC measurements on the performance after feature selection increase by 2.3% to 9.7% and 0.05 to 0.19, respectively. The proposed method is compared with three state-of-the-art predictors using independent test and 10-fold cross-validations, and our method outperforms in all datasets, especially improving the ACC by 3.02% to 7.89% and MCC by 0.06 to 0.15 in the independent test. Two detailed case studies by the proposed method have confirmed the excellent overall performance and correctly identified 24 of 26 4mC sites from the C.elegans gene, and 126 out of 137 4mC sites from the D.melanogaster gene. Conclusions The results show that the proposed feature space and learning algorithm with feature selection can improve the performance of DNA 4mC prediction on the benchmark datasets. The two case studies prove the effectiveness of our method in practical situations.

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