Error Checking for Chinese Query by Mining Web Log

Abstract EN

For the search engine, error-input query is a common phenomenon.

This paper uses web log as the training set for the query error checking.

Through the n-gram language model that is trained by web log, the queries are analyzed and checked.

Some features including query words and their number are introduced into the model.

At the same time data smoothing algorithm is used to solve data sparseness problem.

It will improve the overall accuracy of the n-gram model.

The experimental results show that it is effective.