-
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
-
Dubin, D.: Dimensions and discriminability (1998)
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- Date
- 22. 9.1997 19:16:05
- Imprint
- Urbana-Champaign, IL : Illinois University at Urbana-Champaign, Graduate School of Library and Information Science
- Source
- Visualizing subject access for 21st century information resources: Papers presented at the 1997 Clinic on Library Applications of Data Processing, 2-4 Mar 1997, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign. Ed.: P.A. Cochrane et al
-
Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998)
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- Abstract
- The Wolverhampton Web Library (WWLib) is a WWW search engine that provides access to UK based information. The experimental version developed in 1995, was a success but highlighted the need for a much higher degree of automation. An interesting feature of the experimental WWLib was that it organised information according to DDC. Discusses the advantages of classification and describes the automatic classifier that is being developed in Java as part of the new, fully automated WWLib
- Date
- 1. 8.1996 22:08:06
-
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
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.11, S.2265-2277
-
Liu, R.-L.: Context recognition for hierarchical text classification (2009)
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- Abstract
- Information is often organized as a text hierarchy. A hierarchical text-classification system is thus essential for the management, sharing, and dissemination of information. It aims to automatically classify each incoming document into zero, one, or several categories in the text hierarchy. In this paper, we present a technique called CRHTC (context recognition for hierarchical text classification) that performs hierarchical text classification by recognizing the context of discussion (COD) of each category. A category's COD is governed by its ancestor categories, whose contents indicate contextual backgrounds of the category. A document may be classified into a category only if its content matches the category's COD. CRHTC does not require any trials to manually set parameters, and hence is more portable and easier to implement than other methods. It is empirically evaluated under various conditions. The results show that CRHTC achieves both better and more stable performance than several hierarchical and nonhierarchical text-classification methodologies.
- Date
- 22. 3.2009 19:11:54
- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.803-813
-
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
- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.3, S.431-442
-
Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007)
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- Abstract
- The proliferation of digital resources and their integration into a traditional library setting has created a pressing need for an automated tool that organizes textual information based on library classification schemes. Automated text classification is a research field of developing tools, methods, and models to automate text classification. This article describes the current popular approach for text classification and major text classification projects and applications that are based on library classification schemes. Related issues and challenges are discussed, and a number of considerations for the challenges are examined.
- Date
- 22. 9.2008 18:31:54
-
Mengle, S.; Goharian, N.: Passage detection using text classification (2009)
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- Abstract
- Passages can be hidden within a text to circumvent their disallowed transfer. Such release of compartmentalized information is of concern to all corporate and governmental organizations. Passage retrieval is well studied; we posit, however, that passage detection is not. Passage retrieval is the determination of the degree of relevance of blocks of text, namely passages, comprising a document. Rather than determining the relevance of a document in its entirety, passage retrieval determines the relevance of the individual passages. As such, modified traditional information-retrieval techniques compare terms found in user queries with the individual passages to determine a similarity score for passages of interest. In passage detection, passages are classified into predetermined categories. More often than not, passage detection techniques are deployed to detect hidden paragraphs in documents. That is, to hide information, documents are injected with hidden text into passages. Rather than matching query terms against passages to determine their relevance, using text-mining techniques, the passages are classified. Those documents with hidden passages are defined as infected. Thus, simply stated, passage retrieval is the search for passages relevant to a user query, while passage detection is the classification of passages. That is, in passage detection, passages are labeled with one or more categories from a set of predetermined categories. We present a keyword-based dynamic passage approach (KDP) and demonstrate that KDP outperforms statistically significantly (99% confidence) the other document-splitting approaches by 12% to 18% in the passage detection and passage category-prediction tasks. Furthermore, we evaluate the effects of the feature selection, passage length, ambiguous passages, and finally training-data category distribution on passage-detection accuracy.
- Date
- 22. 3.2009 19:14:43
- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.4, S.814-825
-
Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013)
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- Date
- 23. 3.2013 13:22:36
- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.844-860
-
Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015)
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- Date
- 4. 8.2015 19:22:04
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1817-1831
-
Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003)
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- Abstract
- This study seeks to find out how human beings cluster Web pages naturally. Twenty Web pages retrieved by the Northem Light search engine for each of 10 queries were sorted by 3 subjects into categories that were natural or meaningful to them. lt was found that different subjects clustered the same set of Web pages quite differently and created different categories. The average inter-subject similarity of the clusters created was a low 0.27. Subjects created an average of 5.4 clusters for each sorting. The categories constructed can be divided into 10 types. About 1/3 of the categories created were topical. Another 20% of the categories relate to the degree of relevance or usefulness. The rest of the categories were subject-independent categories such as format, purpose, authoritativeness and direction to other sources. The authors plan to develop automatic methods for categorizing Web pages using the common categories created by the subjects. lt is hoped that the techniques developed can be used by Web search engines to automatically organize Web pages retrieved into categories that are natural to users. 1. Introduction The World Wide Web is an increasingly important source of information for people globally because of its ease of access, the ease of publishing, its ability to transcend geographic and national boundaries, its flexibility and heterogeneity and its dynamic nature. However, Web users also find it increasingly difficult to locate relevant and useful information in this vast information storehouse. Web search engines, despite their scope and power, appear to be quite ineffective. They retrieve too many pages, and though they attempt to rank retrieved pages in order of probable relevance, often the relevant documents do not appear in the top-ranked 10 or 20 documents displayed. Several studies have found that users do not know how to use the advanced features of Web search engines, and do not know how to formulate and re-formulate queries. Users also typically exert minimal effort in performing, evaluating and refining their searches, and are unwilling to scan more than 10 or 20 items retrieved (Jansen, Spink, Bateman & Saracevic, 1998). This suggests that the conventional ranked-list display of search results does not satisfy user requirements, and that better ways of presenting and summarizing search results have to be developed. One promising approach is to group retrieved pages into clusters or categories to allow users to navigate immediately to the "promising" clusters where the most useful Web pages are likely to be located. This approach has been adopted by a number of search engines (notably Northem Light) and search agents.
- Date
- 12. 9.2004 9:56:22
-
Subramanian, S.; Shafer, K.E.: Clustering (2001)
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- Date
- 5. 5.2003 14:17:22
-
Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999)
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- Imprint
- Hinskey Hill : Learned Information
- Source
- Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
-
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
-
Möller, G.: Automatic classification of the World Wide Web using Universal Decimal Classification (1999)
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- Imprint
- Hinskey Hill : Learned Information
- Source
- Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
-
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.
- Source
- Information processing and management. 39(2003) no.2, S.195-213
-
Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015)
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- Abstract
- Text classification (TC) is a core technique for text mining and information retrieval. It has been applied to many applications in many different research and industrial areas. Term-weighting schemes assign an appropriate weight to each term to obtain a high TC performance. Although term weighting is one of the important modules for TC and TC has different peculiarities from those in information retrieval, many term-weighting schemes used in information retrieval, such as term frequency-inverse document frequency (tf-idf), have been used in TC in the same manner. The peculiarity of TC that differs most from information retrieval is the existence of class information. This article proposes a new term-weighting scheme that uses class information using positive and negative class distributions. As a result, the proposed scheme, log tf-TRR, consistently performs better than do other schemes using class information as well as traditional schemes such as tf-idf.
- Source
- Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2553-2565
-
Rijsbergen, C.J. van: Automatic classification in information retrieval (1978)
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-
Kwok, K.L.: ¬The use of titles and cited titles as document representations for automatic classification (1975)
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- Source
- Information processing and management. 11(1975), S.201-206
-
Schiminovich, S.: Automatic classification and retrieval of documents by means of a bibliographic pattern discovery algorithm (1971)
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- Source
- Information storage and retrieval. 6(1971), S.417-435
