-
Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004)
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- Content
- Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
- Date
- 8. 1.2013 10:22:32
-
Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998)
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- Date
- 1. 8.1996 22:08:06
-
Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006)
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- Date
- 22. 7.2006 16:24:52
-
Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013)
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- Abstract
- Retrieval of disease information is often based on several key aspects such as etiology, diagnosis, treatment, prevention, and symptoms of diseases. Automatic identification of disease aspect information is thus essential. In this article, I model the aspect identification problem as a text classification (TC) problem in which a disease aspect corresponds to a category. The disease aspect classification problem poses two challenges to classifiers: (a) a medical text often contains information about multiple aspects of a disease and hence produces noise for the classifiers and (b) text classifiers often cannot extract the textual parts (i.e., passages) about the categories of interest. I thus develop a technique, PETC (Passage Extractor for Text Classification), that extracts passages (from medical texts) for the underlying text classifiers to classify. Case studies on thousands of Chinese and English medical texts show that PETC enhances a support vector machine (SVM) classifier in classifying disease aspect information. PETC also performs better than three state-of-the-art classifier enhancement techniques, including two passage extraction techniques for text classifiers and a technique that employs term proximity information to enhance text classifiers. The contribution is of significance to evidence-based medicine, health education, and healthcare decision support. PETC can be used in those application domains in which a text to be classified may have several parts about different categories.
- Date
- 28.10.2013 19:22:57
-
Shafer, K.E.: Automatic Subject Assignment via the Scorpion System (2001)
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- Footnote
- Teil eines Themenheftes: OCLC and the Internet: An Historical Overview of Research Activities, 1990-1999 - Part I
-
Subramanian, S.; Shafer, K.E.: Clustering (2001)
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- Date
- 5. 5.2003 14:17:22
-
HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016)
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- Date
- 1. 2.2016 18:25:22
-
Miyamoto, S.: Information clustering based an fuzzy multisets (2003)
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- Abstract
- A fuzzy multiset model for information clustering is proposed with application to information retrieval on the World Wide Web. Noting that a search engine retrieves multiple occurrences of the same subjects with possibly different degrees of relevance, we observe that fuzzy multisets provide an appropriate model of information retrieval on the WWW. Information clustering which means both term clustering and document clustering is considered. Three methods of the hard c-means, fuzzy c-means, and an agglomerative method using cluster centers are proposed. Two distances between fuzzy multisets and algorithms for calculating cluster centers are defined. Theoretical properties concerning the clustering algorithms are studied. Illustrative examples are given to show how the algorithms work.
-
Cheng, P.T.K.; Wu, A.K.W.: ACS: an automatic classification system (1995)
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- Abstract
- In this paper, we introduce ACS, an automatic classification system for school libraries. First, various approaches towards automatic classification, namely (i) rule-based, (ii) browse and search, and (iii) partial match, are critically reviewed. The central issues of scheme selection, text analysis and similarity measures are discussed. A novel approach towards detecting book-class similarity with Modified Overlap Coefficient (MOC) is also proposed. Finally, the design and implementation of ACS is presented. The test result of over 80% correctness in automatic classification and a cost reduction of 75% compared to manual classification suggest that ACS is highly adoptable
-
Ingwersen, P.; Wormell, I.: Ranganathan in the perspective of advanced information retrieval (1992)
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-
Fagni, T.; Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison (2010)
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- Abstract
- Hierarchical text classification (HTC) approaches have recently attracted a lot of interest on the part of researchers in human language technology and machine learning, since they have been shown to bring about equal, if not better, classification accuracy with respect to their "flat" counterparts while allowing exponential time savings at both learning and classification time. A typical component of HTC methods is a "local" policy for selecting negative examples: Given a category c, its negative training examples are by default identified with the training examples that are negative for c and positive for the categories which are siblings of c in the hierarchy. However, this policy has always been taken for granted and never been subjected to careful scrutiny since first proposed 15 years ago. This article proposes a thorough experimental comparison between this policy and three other policies for the selection of negative examples in HTC contexts, one of which (BEST LOCAL (k)) is being proposed for the first time in this article. We compare these policies on the hierarchical versions of three supervised learning algorithms (boosting, support vector machines, and naïve Bayes) by performing experiments on two standard TC datasets, REUTERS-21578 and RCV1-V2.
-
Godby, C. J.; Stuler, J.: ¬The Library of Congress Classification as a knowledge base for automatic subject categorization (2001)
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-
Dubin, D.: Dimensions and discriminability (1998)
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- Date
- 22. 9.1997 19:16:05
-
Automatic classification research at OCLC (2002)
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- Date
- 5. 5.2003 9:22:09
-
Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007)
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- Date
- 22. 9.2008 18:31:54
-
Díaz, I.; Ranilla, J.; Montañes, E.; Fernández, J.; Combarro, E.F.: Improving performance of text categorization by combining filtering and support vector machines (2004)
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-
Na, J.-C.; Sui, H.; Khoo, C.; Chan, S.; Zhou, Y.: Effectiveness of simple linguistic processing in automatic sentiment classification of product reviews (2004)
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-
Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012)
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- Abstract
- We study the problem of question topic classification using a very large real-world Community Question Answering (CQA) dataset from Yahoo! Answers. The dataset comprises 3.9 million questions and these questions are organized into more than 1,000 categories in a hierarchy. To the best knowledge, this is the first systematic evaluation of the performance of different classification methods on question topic classification as well as short texts. Specifically, we empirically evaluate the following in classifying questions into CQA categories: (a) the usefulness of n-gram features and bag-of-word features; (b) the performance of three standard classification algorithms (naive Bayes, maximum entropy, and support vector machines); (c) the performance of the state-of-the-art hierarchical classification algorithms; (d) the effect of training data size on performance; and (e) the effectiveness of the different components of CQA data, including subject, content, asker, and the best answer. The experimental results show what aspects are important for question topic classification in terms of both effectiveness and efficiency. We believe that the experimental findings from this study will be useful in real-world classification problems.
-
Bianchini, C.; Bargioni, S.: Automated classification using linked open data : a case study on faceted classification and Wikidata (2021)
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-
Liu, R.-L.: Context recognition for hierarchical text classification (2009)
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- Date
- 22. 3.2009 19:11:54
