Supervised Text Classification Task
(Redirected from Supervised Text Item Classification Task)
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A Supervised Text Classification Task is a data-driven text classification task that is a supervised classification task.
- AKA: Supervised Text Categorization.
- Context:
- It can be solved by a Supervised Text Classification System (that implements a Supervised Text Classification Algorithm.
- It can range from being a Supervised Binary Text Classification Task to being a Supervised Multiclass Text Classification Task.
- It can range from being a Supervised Unilabel Text Classification Task to being a Supervised Multilabel Text Classification Task.
- …
- Counter-Example(s):
- See: Supervised Document Classification, Supervised String Classification.
References
2009
- (Bird et al., 2009) ⇒ Steven Bird, Ewan Klein, and Edward Loper. (2009). “Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit. O'Reilly
2008
- (Manning et al., 2008) ⇒ Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. (2008). “Introduction to Information Retrieval." Cambridge University Press. ISBN:0521865719.
- The text classification problem [1] ... In text classification, we are given a description [math]\displaystyle{ d }[/math] in
X
of a document, whereX
is the document space ; and a fixed set of classesC
= {c1,c2,...,cJ}. Classes are also called categories or labels. Typically, the document spaceX
is some type of high-dimensional space, and the classes are human defined for the needs of an application... We are given a training setD
of labeled documents <d,c>, where <d,c> inX
xC
.Our goal in text classification is high accuracy on test data or new data ... When we use the training set to learn a classifier for test data, we make the assumption that training data and test data are similar or from the same distribution.
- The text classification problem [1] ... In text classification, we are given a description [math]\displaystyle{ d }[/math] in
2007
- (Thet et al., 2007) ⇒ Tun Thura Thet, Jin-Cheon Na, and Christopher S. G. Khoo. (2007). “Filtering Product Reviews from Web Search Results.” In: Proceedings of the 2007 ACM symposium on Document Engineering.
- The Search Snippets are from Google queries using the format “[product name] review”.