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This paper compares the performance of keyword and machine learning-based chest x-ray report classification for Acute Lung Injury (ALI). ALI mortality is approximately 30 percent. High mortality is, in part, a consequence of delayed manual chest x-ray classification. An automated system could reduce the time to
Online advertising has now turned to be one of the major revenue sources for today's Internet companies. Among the different channels of advertising, contextual advertising takes the great part. There are already lots of studies done for the keyword extraction problem in contextual advertising for English, however
This paper describes a mechanism of defining keywords on our Information Utilization System, which we have proposed to utilize information received via e-mail. Our system utilizes metadata of e-mail messages to organize pieces of information, and keywords are used to classify pieces of information based on their
In this paper, a method of automatic Chinese keyword extraction based on KNN is proposed. Firstly, it preprocesses the document by vector space model. Secondly, it constructs a set of candidate keywords based on KNN method and the labeled dataset. Finally, it post-processes on candidate keywords by the character of
This paper addresses the problem of clustering dynamic collections of web documents. We show an iterative algorithm based on a fine-grained keyword extraction (simple, compound words and proper nouns). Each new document inserted in the collection is either assigned to an existing class containing documents of the same
attributes must be shared to have at every node a more accurate estimation of the global classifier. When expanding the knowledge of the local classifiers, to reduce costs, the network traffic should be kept to a minimum. We propose a probabilistic model for a keyword selection method which makes a more thorough analysis
including citation function classification, sentiment analysis and keyword extraction. A concrete case of CSLN in opinion mining discipline is studied. Based on the exploration of CSLN from multi-perspective, we can effectively find articles with high importance, detect opinion communities and discover emergent topics among
. A third technique involves extraction of keywords and storing them in a properly indexed base. These then can serve the dual purpose of providing solutions to Lazy Learning classification for automatic subject-wise archiving and formation of relevant word sequences for detection of plagiarism using Association Rule
This paper presents a novel framework for multi-folder email classification using graph mining as the underlying technique. Although several techniques exist (e.g., SVM, TF-IDF, n-gram) for addressing this problem in a delimited context, they heavily rely on extracting high-frequency keywords, thus ignoring the
of cultural information. Therefore, text categorization research has become more important. The paper improved the precision of the traditional text categorization by the process that we mended the weight of words and mined potential keywords, then found their relationship. In the end of the paper, an experiment was
The purpose of this research is to propose an appropriate classification approach to improving the effectiveness of spam filtering on the issue of skewed class distributions. A clustering-based classifier is proposed to first cluster documents into several groups, and then an equal number of keywords are extracted
Traditional SMS filters are basically text-based filtering. However, the filtering mechanisms have limitations: first, the keyword can easily be replaced by other symbols, which increase the difficulty of filtering; second, is garbage in the ongoing renovation of keywords, filtering mechanism of the high demands of
This paper discusses a approach of Chinese text classification on semantic Web. It is given one classified technology based on the semantic concept established on the "How-net" . It extracts keywords from text, analyses the full text using the keywords concept, and then the integrates to classify by categories of
This article proposes such a question classification approach that integrates multiple semantic features. It is aimed at these two questions in Chinese question classification models: inaccurate semantic information extraction and too slow processing speed caused by too high Eigenvector dimension. With the help of HowNet and the support vector machine and syntactic and semantic information of question...
Abstract-By analyzing the process of classification and MapReduce computing paradigms, it is found that the parallel and distributed computing model in MapReduce is appropriate for constructing classifier model. This paper presents a MapReduce algorithm for parallel and distributed classification, aiming to reduce the computational time in training process on large scale documents. Our experiment...
In this paper, we propose an evolutionary approach to rank association rules for classification. The association rules are ranked by their support, confidence and length in one of the most important associative classification method, Classification based on Multiple Association Rule (CMAR). However, from some empirical studies, we find that if the rules are ranked by some equations first, the classification...
Clients' queries upon keywords or other informed description do not usually provide complete and unambiguous retrieval of information. Expansion of the queries based on semantic relation and phrase patterns is an effective approach to improve the retrieval. In this paper, a novel approach to queries expansion is
source, specifically Yahoo's ldquosuggested keywordsrdquo. These keywords are based on co-occurrence data across queries. The classifier, which is built offline with training data, makes use of the top-n results during training, but not duing testing. Thus, there is an asymmetry between the training and testing data. We
digital library based on topic or concept features. Firstly, documents in a special domain are automatically produced by document classification approach. It integrates the rule-based and statistical method to classify the documents in the large-scale collection. Then, the keywords of each document are extracted using the
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