Classifying the Arabic Language Texts Part 2

There are many areas for subjects such not medicine, sports, health, law, etc., although not process not classification texts we facilitate search and tell us where should be to search, so do not waste time in retrieving information I have no relation of the request.

The text classification is the automated technique used to classify the text in predefined category which is more related to the text.

in this day the manual classification become very difficult with the huge data that's uploaded daily on the internet and it's need long time, in other words we can say it's impossible work in the internet world, so the automated text classification technique make the classify Process very simple and faster.

Most research has focused on classification texts written in the English Language more than the Arabic language because of the Arabic nature and the difficulty of their structures the difficult nature in the Arabic language make it more complex and difficult to deal with them because of the many rules and anomalous characteristics, but it has become necessary to deal with this language because of the wide spread over the Internet.

To facilitate the search and retrieval in the Arabic language there are many algorithms working on the text classification that helps to retrieve data related to research in a short time and more accurate

In this thesis we have studied many classification algorithms of Arabic-language texts.

There are many algorithms used for classification, but any of them better?.

so we chose some of the text and classification algorithms and we have applied it's to the dataset written in Arabic language, each of these algorithms have the characteristics and standards, such as precision, Recall, F-measure an...

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...yclic graphs whose nodes represent random variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes which are not connected represent variables which are conditionally independent of each other. Each node is associated with a probability function that takes as input a particular set of values for the node's parent variables and gives the probability of the variable represented by the node. For example, if the parents are Boolean variables then the probability function could be represented by a table of entries, one entry for each of the possible combinations of its parents being true or false, combinations of its parents being true or false. Similar ideas may be applied to undirected, and possibly cyclic, graphs; such are called Markov networks.