Supervised Text Classification Task

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):
  • a Supervised Email Classification Task, such email records (with email author, email subject, etc.).
  • a Heuristic Text Classification Task.
  • a Supervised Structured Document Classification Task.
  • a Supervised DNA Classification Task.
  • a Supervised Text Segmentation Task.
• See: Supervised Document Classification, Supervised String Classification.

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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
  • http://www.nltk.org/book/ch06.html

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, where X is the document space ; and a fixed set of classes C = {c₁,c₂,...,c_J}. Classes are also called categories or labels. Typically, the document space X is some type of high-dimensional space, and the classes are human defined for the needs of an application... We are given a training set D of labeled documents <d,c>, where <d,c> in X x C.

    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.

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”.

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