Abstract :
In this paper, Web search engine is discussed. At the core of most search engines is the concept of word frequency: words that pop-up more regularly in a document tend to score more highly in rankings when a user searches. However, there are common words that are found frequently in lots of documents. Words like those are not very useful in finding the document you want. Microsoft´s Live search engine combines a number of techniques which are combined using a neural network. This neural network is meant to learn how techniques can best be combined to deliver what users want to find. There are other machine-learning approaches now in use. Some search-engine builders, such as autonomy, have gone back to probabilistic techniques, but in place of frequency have used algorithms that allow the machine to learn how text is structured statistically. These engines use Bayesian inference to process the text: computing the probability of one word following another.
