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UPCA for .NET sentiment detection in Software Deploy Code39 in Software sentiment detection

sentiment detection using none tobuild none for asp.net web,windows applicationasp.net upc-a generator email sorting QR Code Module Size and Area vertical search engine This list shows the general i none for none mportance of classi cation in IR. Most retrieval systems today contain multiple components that use some form of classi er..

P1: KRU/IRP irbook CUUS232/Ma nning 978 0 521 86571 5 May 27, 2008 12:8. Text classi cation and Naive Bayes rules in text classi cation statistical text classi cation labeling The classi cation task we wil none none l use as an example in this book is text classi cation. A computer is not essential for classi cation. Many classi cation tasks have traditionally been solved manually.

Books in a library are assigned Library of Congress categories by a librarian. But manual classi cation is expensive to scale. The multicore computer chips example illustrates one alternative approach: classi cation by the use of standing queries which can be thought of as rules most commonly written by hand.

As in our example (multicore OR multi-core) AND (chip OR processor OR microprocessor), rules are sometimes equivalent to Boolean expressions. A rule captures a certain combination of keywords that indicates a class. Hand-coded rules have good scaling properties, but creating and maintaining them over time is labor intensive.

A technically skilled person (e.g., a domain expert who is good at writing regular expressions) can create rule sets that will rival or exceed the accuracy of the automatically generated classi ers we will discuss shortly; however, it can be hard to nd someone with this specialized skill.

Apart from manual classi cation and hand-crafted rules, there is a third approach to text classi cation, namely, machine learning-based text classi cation. It is the approach that we focus on in the next several chapters. In machine learning, the set of rules or, more generally, the decision criterion of the text classi er, is learned automatically from training data.

This approach is also called statistical text classi cation if the learning method is statistical. In statistical text classi cation, we require a number of good example documents (or training documents) for each class. The need for manual classi cation is not eliminated because the training documents come from a person who has labeled them where labeling refers to the process of annotating each document with its class.

But labeling is arguably an easier task than writing rules. Almost anybody can look at a document and decide whether or not it is related to China. Sometimes such labeling is already implicitly part of an existing work ow.

For instance, you may go through the news articles returned by a standing query each morning and give relevance feedback (cf. 9) by moving the relevant articles to a special folder like multicore-processors. We begin this chapter with a general introduction to the text classi cation problem including a formal de nition (Section 13.

1); we then cover Naive Bayes, a particularly simple and effective classi cation method (Sections 13.2 13.4).

All of the classi cation algorithms we study represent documents in high-dimensional spaces. To improve the ef ciency of these algorithms, it is generally desirable to reduce the dimensionality of these spaces; to this end, a technique known as feature selection is commonly applied in text classi cation as discussed in Section 13.5.

Section 13.6 covers evaluation of text classi cation. In the following chapters, s 14 and 15,.

P1: KRU/IRP irbook CUUS232/Ma nning 978 0 521 86571 5 May 27, 2008 12:8. 13.1 The text classi cation problem we look at two other families of classi cation methods, vector space classi ers and support vector machines.. 13.1 The text classi cation problem In text classi cation, we are given a description d X of a document, where document X is the document sp none none ace; and a xed set of classes C = {c 1 , c 2 , . . .

, c J }. Classes space are also called categories or labels. Typically, the document space X is some class.

type of high-dimensional spac e, and the classes are human de ned for the needs of an application, as in the examples China and documents that talk about training set multicore computer chips above. We are given a training set D of labeled documents d, c ,where d, c X C. For example: d, c = Beijing joins the World Trade Organization, China for the one-sentence document Beijing joins the World Trade Organization and the class (or label) China.

learning Using a learning method or learning algorithm, we then wish to learn a clasmethod si er or classi cation function that maps documents to classes: (13.1) :X C.
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