A text retrieval system for language learning returns reading materials at the appropriate difficulty level for the user. The system typically maintains a learner model on the user's vocabulary knowledge, and identifies texts that best fit the model.
As the user's language proficiency increases, model updates are necessary to retrieve texts with the corresponding lexical complexity. We investigate an open learner model that allows user modification of its content, and evaluate its effectiveness with respect to the amount of user update effort. We compare this model with the graded approach, in which the system returns texts at the optimal grade. When the user makes at least half of the expected updates to the open learner model, simulation results show that it outperforms the graded approach in retrieving texts that fit user preference for new-word density.
This research designs web search engine kernel overrule in searching of specific
fields and indexing indicated sites.
This research contain information about search in web , retrieval system , types of
search engines and basic architectures of bui
lding search engines .It suggests search engine
architecture kernel of dedicated search engine to do final planner of search engine
architecture ,and build parts of search engine and execute test to get results .
