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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
-
Subramanian, S.; Shafer, K.E.: Clustering (2001)
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- Date
- 5. 5.2003 14:17:22
- Footnote
- Teil eines Themenheftes: OCLC and the Internet: An Historical Overview of Research Activities, 1990-1999 - Part II
-
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
-
Liu, R.-L.: Context-based term frequency assessment for text classification (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.2, S.300-309
- Year
- 2010
-
Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
- Year
- 2010
-
Shafer, K.E.: Evaluating Scorpion Results (2001)
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- Footnote
- Teil eines Themenheftes: OCLC and the Internet: An Historical Overview of Research Activities, 1990-1999 - Part II
-
Kishida, K.: High-speed rough clustering for very large document collections (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1092-1104
- Year
- 2010
-
HaCohen-Kerner, Y.; Beck, H.; Yehudai, E.; Rosenstein, M.; Mughaz, D.: Cuisine : classification using stylistic feature sets and/or name-based feature sets (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1644-1657
- Year
- 2010
-
Fagni, T.; Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2256-2265
- Year
- 2010
-
Groß, T.; Faden, M.: Automatische Indexierung elektronischer Dokumente an der Deutschen Zentralbibliothek für Wirtschaftswissenschaften : Bericht über die Jahrestagung der Internationalen Buchwissenschaftlichen Gesellschaft (2010)
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- Abstract
- Mit der Anfang 2010 begonnen Implementierung und Ergebnisevaluierung des automatischen Indexierungsverfahrens "Decisiv Categorization" der Firma Recommind soll das hier skizzierte Informationsstrukturierungsproblem in zwei Schritten gelöst werden. Kurz- bis mittelfristig soll die intellektuelle Indexierung durch ein semiautomatisches Verfahren6 unterstützt werden. Mittel- bis langfristig soll das maschinelle Verfahren, aufbauend auf einem entsprechenden Training, in die Lage versetzt werden, sowohl im Hause vorliegende Dokumente vollautomatisch zu indexieren als auch ZBW-fremde digitale Informationsressourcen zu verschlagworten bzw. zu klassifizieren, um sie in einem gemeinsamen Suchraum auffindbar machen zu können. Im Anschluss an diese Einleitung werden die ersten Ansätze maschineller Sacherschließung an der ZBW (2001-2004) und deren Ergebnisse und Problemlagen aufgezeigt. Danach werden die Rahmenbedingungen (Projektauftrag und -ziel) für eine Wiederaufnahme des Vorhabens im Jahre 2009 aufgezeigt, gefolgt von einer Darstellung der Funktionsweise der Recommind-Technologie und deren Einsatz im Rahmen der Sacherschließung von Online-Dokumenten mit einem Thesaurus. Schwerpunkt dieser Abhandlung bilden im Anschluss daran die Evaluierungsmöglichkeiten automatischer Indexierungsansätze sowie die aktuellen Ergebnisse und zentralen Erkenntnisse des Einsatzes im Kontext der ZBW. Das Fazit beschreibt die entsprechenden Schlussfolgerungen aus den erzielten Ergebnissen sowie den Ausblick auf das weitere Vorgehen.
- Source
- Bibliotheksdienst. 44(2010) H.12, S.1120-1135
- Year
- 2010
-
Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009)
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- Date
- 22. 8.2009 12:54:24
-
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
-
Ru, C.; Tang, J.; Li, S.; Xie, S.; Wang, T.: Using semantic similarity to reduce wrong labels in distant supervision for relation extraction (2018)
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- Abstract
- Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms.
-
Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989)
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- Pages
- S.1-22
-
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
-
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
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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
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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
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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
