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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.
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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
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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
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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
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Liu, R.-L.: Context-based term frequency assessment for text classification (2010)
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- Abstract
- Automatic text classification (TC) is essential for the management of information. To properly classify a document d, it is essential to identify the semantics of each term t in d, while the semantics heavily depend on context (neighboring terms) of t in d. Therefore, we present a technique CTFA (Context-based Term Frequency Assessment) that improves text classifiers by considering term contexts in test documents. The results of the term context recognition are used to assess term frequencies of terms, and hence CTFA may easily work with various kinds of text classifiers that base their TC decisions on term frequencies, without needing to modify the classifiers. Moreover, CTFA is efficient, and neither huge memory nor domain-specific knowledge is required. Empirical results show that CTFA successfully enhances performance of several kinds of text classifiers on different experimental data.
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Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013)
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- Date
- 28.10.2013 19:22:57
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Yang, P.; Gao, W.; Tan, Q.; Wong, K.-F.: ¬A link-bridged topic model for cross-domain document classification (2013)
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- Source
- Information processing and management. 49(2013) no.6, S.1181-1193
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Borodin, Y.; Polishchuk, V.; Mahmud, J.; Ramakrishnan, I.V.; Stent, A.: Live and learn from mistakes : a lightweight system for document classification (2013)
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- Source
- Information processing and management. 49(2013) no.1, S.83-98
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Wang, H.; Hong, M.: Supervised Hebb rule based feature selection for text classification (2019)
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- Source
- Information processing and management. 56(2019) no.1, S.167-191
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Yilmaz, T.; Ozcan, R.; Altingovde, I.S.; Ulusoy, Ö.: Improving educational web search for question-like queries through subject classification (2019)
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- Source
- Information processing and management. 56(2019) no.1, S.228-246
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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
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Altinel, B.; Ganiz, M.C.: Semantic text classification : a survey of past and recent advances (2018)
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- Source
- Information processing and management. 54(2018) no.6, S.1129-1153
