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Current search engines are not very effective in filtering out harmful information since the technology they use for filtering is often based on traditional text classification in which texts are often classified according to feature words. To improve the effectiveness of filtering, in this paper, we propose a new filtering scheme in which we combine the neural network and ontology categorization...
In this paper, a drug knowledge platform based on intelligent information technology is constructed. Unlike the traditional virtual drug compound library which focus on the patents of drug only, the aim of the new platform is to design a system including information retrieval, information extraction, construction of drug compound and drug ontology, structure based virtual screening, and text classification...
Feature selection is of paramount concern in document classification process which improves the efficiency and accuracy of text classifier. Vector Space Model is used to represent the ??Bag of Word?? BOW of the documents with term weighting phenomena. Documents representing through this model has some limitations that is, ignoring term dependencies, structure and ordering of the terms in documents...
In the last few years, several works in the literature of software engineering have addressed the problem of requirement management. A majority problem of software errors is introduced during the requirements phase because much of requirements specification is written in natural language format. As this, it is hard to identify consistencies because of too ambiguous for specification purpose. Therefore,...
Current classification methods are based on the ldquobag of wordsrdquo (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. In this paper, we proposed a system that uses ontologies and natural language processing techniques to index texts. Traditional BOW matrix is replaced by ldquoBag of Conceptsrdquo (BOC)...
Ontology learning has become a major area of research whose goal is to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing...
Traditional discriminative classification method makes little attempt to reveal the probabilistic structure and the correlation within both input and output spaces. In the scenario of multi-label classification, most of the classifiers simply assume the predefined classes are independently distributed, which would definitely hinder the classification performance when there are intrinsic correlations...
Ontology can provide a powerful representation of information space and solve many semantic problems. It is wonderful to apply ontology to text classification. This paper proposes a general framework for text classification, which can overcome the limitations of traditional text classification methods. The results of experiment prove that the general framework is applicable across different domains...
The following topics are dealt with: grid computing; semantic Web service; collaborative work; ontology; data mining; information retrieval; text classification and resource management.
Huge biomedical literatures result in many new challenges on text classification, its efficiency and sparseness of data attract many researchers. Recent success of language modeling in information retrieval have let us consider again about multinomial Naive Bayes for text classification. In this paper, we propose a semantic smoothing method for Naive Bayes model, biomedical documents were indexed...
The focus of the research is to disambiguate search query by categorizing search results returned by search engines and interacting with the user to achieve query and results refinement. A novel special search-browser has been developed which combines search engine results, the open directory project (ODP) based lightweight ontology as navigator and classifier, and search results categorizing. Categories...
Text classification is becoming one of the key techniques in organizing and handling a large amount of text data. In this paper, a combination of ontology with statistical method is presented to improve the precision of text classification. In this study, first, different kind of linguistic ontology knowledge will be respectively acquired by learning training corpus to determine text classifiers....
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