-
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
-
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
-
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.
-
Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013)
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- Date
- 28.10.2013 19:22:57
-
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
-
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
-
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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- 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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- 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
