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In this research, we propose a particular version of KNN (K Nearest Neighbor) where the similarity between feature vectors is computed considering the similarity among attributes or features as well as one among values. The task of text summarization is viewed into the binary classification task where each paragraph or sentence is classified into the essence or non-essence, and in previous works,...
In this research, we propose the version of K Nearest Neighbor which considers similarity among attributes for computing the similarity between feature vectors. The text segmentation task is viewed into the binary classification where each pair of sentences or paragraphs is classified into whether we put the boundary or not, and the proposed version resulted in the successful results in previous works...
In this research, we propose the table based KNN as the approach to the text categorization. In previous works, we discovered that encoding texts into tables improved the performance in the text categorization, so in this research, become to consider the possibility of encoding words into tables as well as texts. In this research, we encode words into tables where entries are texts and their weights,...
This research proposes the table based AHC algorithm as the approach to the word clustering task. The results from encoding texts into tables were successful in the previous works on the text categorization and the text clustering, and if oppositely to the case of the text encoding, texts are assumed to be elements of each word, it becomes to be possible to encode words into tables. In this research,...
In text categorization, the dimensionality reduction methods, such as latent semantic indexing and nonnegative matrix factorization, commonly yield the dense representation that is not consistent with our common knowledge. On the other hand, the popular sparse coding methods are time-consuming and their dictionaries might contain negative entries, which is difficulty to interpret the semantic meaning...
As an important means of monitoring public opinion through Internet or other languages media, real-time construction and supplementary of sensitive information is important for language information processing monitoring. Because the data of network has a large degree of freedom and has large information capacity and they are difficult to be controlled by the Government. In order to monitor Tibetan...
Every day, the mass of information available to us increases. This information would be irrelevant if our ability to efficiently access did not increase as well. For maximum benefit, we need tools that allow us to search, sort, index, store, and analyze the available data. We also need tools helping us to find in a reasonable time the desired information by performing certain tasks for us. One of...
Because of the critical role that communication plays in a team's ability to coordinate action, the measurement and analysis of online transcripts in order to predict team performance is becoming increasingly important in domains such as global software development. Current approaches rely on human experts to classify and compare groups according to some prescribed categories, resulting in a laborious...
Error-correcting output code (ECOC) is an effective approach to solve the problem of multiclass SVM. In this paper, a probabilistic approach that is based on ECOC is proposed. In the training stage, a coding scheme is predefined, and a special model is trained by samples. In the classification stage, besides the labels from SVM as usual, posterior probabilities of labels are also calculated. They...
In this research, we propose the similarity matrix based version of NTSO as the approach to the text clustering. For using one of traditional approaches to text clustering, documents should be encoded into numerical vectors; encoding so causes the two main problems: the huge dimensionality and the sparse distribution. In order to solve the problems, in this research, we propose to encode documents...
Text Categorization is an important research branch in the data mining domain. In this paper, an improved Naive Bayesian Classifier which is based on the Genetic Algorithms is proposed. It can make an effective Naive Bayesian classifier with excellent attributes Set in the field of text categorization. The experiments show that this method has a good classification performance.
A major problem with text classification problems is the high dimensionality of the feature space. This paper investigates how genetic algorithm and k-means algorithm can help select relevant features in text classification. which uses the genetic algorithm (GA) optimization features to implement global searching, and uses k-means algorithm to selection operation to control the scope of the search,...
This research proposes the application of NTC (neural text categorizer) for categorizing news articles. Even if the research on text categorization has been progressed very much, documents should be still encoded into numerical vectors. Encoding so causes the two main problems: huge dimensionality and sparse distribution. The idea of this research as the solution to the problems is to encode documents...
Text categorization (TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. The Euclidean distance is usually chosen as the similarity measure in K-nearest neighbor classification algorithm. All the features of each vector have different functions in describing samples. So we can decide different...
In this research, we propose NTC (Neural Text Categorizer) as the approach to text categorization. Traditional approaches to text categorization require encoding documents into numerical vectors which leads to the two main problems: huge dimensionality and sparse distribution in each numerical vector. In this research, documents are encoded into string vectors instead of numerical vectors, and a new...
Analyzing spend transactions is essential to organizations for understanding their global procurement. Central to this analysis is the automated classification of these transactions to hierarchical commodity coding systems. Spend classification is challenging due not only to the complexities of the commodity coding systems but also because of the sparseness and quality of each individual transaction...
Text classification has received extensive attention in recent years, which is an important means of data mining. This paper analyzed basic theory and general structure of text classification, given a text classification method based on improved genetic algorithms, introduced simulated annealing mechanism of genetic algorithm to solve the precocious easy, local optimum, and so on, using the Roocchio...
Web page classification poses new research challenges because of the noisy nature of the pages. For the bilingual Chinese-English web pages, it also needs to be considered that how to extract the terms of different languages exactly. A new dictionary-based multilingual text categorization approach is proposed in this paper to try to classify the Chinese-English web pages in specific domain into a...
This research proposes an alternative approach to machine learning based ones for categorizing news articles given as in plain texts. In order to use one of machine learning based approaches for the task, documents should be encoded into numerical vectors; it causes two problems: huge dimensionality and sparse distribution. The proposed approach is intended to address the two problems. In other words,...
High-dimensional feature space affects the quality and efficiency of text categorization. This paper investigates an improved genetic algorithm that how to help select relevant features in text classification. We follow the so-called "region growing" method to initialize the population, and uses k-means algorithm to selection operation to control the scope of the search, ensure the validity...
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