A cross lingual texts filtering module in classifiable sememes vector space

The WWW is increasingly being used as a source of information. The volume of this information is accessed by users using direct manipulation tools. The paper describes a module that sifts through a large number of texts retrieved by the user. We describe a system that learns a model of the user´s preferences, filters the information, and notifies the user when relevant information becomes available. The user´s model is represented as a vector in the vector space of classifiable sememes. The document is also represented as a vector. The relevance of the text to the user´s interest can be measured by using the cosine angle between the two vectors. Experiments are given to demonstrate it to be a good idea