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Ibekwe-SanJuan, F.; SanJuan, E.: From term variants to research topics (2002)
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- Source
- Knowledge organization. 29(2002) nos.3/4, S.181-197
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Yao, H.; Etzkorn, L.H.; Virani, S.: Automated classification and retrieval of reusable software components (2008)
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- Source
- Journal of the American Society for Information Science and Technology. 59(2008) no.4, S.613-627
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Vilares, D.; Alonso, M.A.; Gómez-Rodríguez, C.: On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages (2015)
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- Date
- 4. 8.2015 19:18:47
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Fang, H.: Classifying research articles in multidisciplinary sciences journals into subject categories (2015)
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- Date
- 4. 9.2015 15:35:34
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AlQenaei, Z.M.; Monarchi, D.E.: ¬The use of learning techniques to analyze the results of a manual classification system (2016)
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- Abstract
- Classification is the process of assigning objects to pre-defined classes based on observations or characteristics of those objects, and there are many approaches to performing this task. The overall objective of this study is to demonstrate the use of two learning techniques to analyze the results of a manual classification system. Our sample consisted of 1,026 documents, from the ACM Computing Classification System, classified by their authors as belonging to one of the groups of the classification system: "H.3 Information Storage and Retrieval." A singular value decomposition of the documents' weighted term-frequency matrix was used to represent each document in a 50-dimensional vector space. The analysis of the representation using both supervised (decision tree) and unsupervised (clustering) techniques suggests that two pairs of the ACM classes are closely related to each other in the vector space. Class 1 (Content Analysis and Indexing) is closely related to Class 3 (Information Search and Retrieval), and Class 4 (Systems and Software) is closely related to Class 5 (Online Information Services). Further analysis was performed to test the diffusion of the words in the two classes using both cosine and Euclidean distance.
-
Chae, G.; Park, J.; Park, J.; Yeo, W.S.; Shi, C.: Linking and clustering artworks using social tags : revitalizing crowd-sourced information on cultural collections (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.4, S.885-899
-
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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- Source
- Information processing and management. 54(2018) no.4, S.593-608
-
Oberhauser, O.: Automatisches Klassifizieren : Entwicklungsstand - Methodik - Anwendungsbereiche (2005)
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- Isbn
- 3-631-53684-4
