Linear feature-based models for information retrieval

Feature Reduction for Information Retrieval

Linear time series models for term weighting in information retrieval

Common measures of term importance in information retrieval (IR) rely on counts of term frequency; rare terms receive higher weight in document ranking than common terms receive. However, realistic scenarios yield additional information about terms in a collection. Of interest in this paper is the temporal behavior of terms as a collection changes over time. We propose capturing each term’s col...

Public Transport Ontology for Passenger Information Retrieval

Passenger information aims at improving the user-friendliness of public transport systems while influencing passenger route choices to satisfy transit user’s travel requirements. The integration of transit information from multiple agencies is a major challenge in implementation of multi-modal passenger information systems. The problem of information sharing is further compounded by the multi-l...

Concept-Based Feature Generation and Selection for Information Retrieval

Traditional information retrieval systems use query words to identify relevant documents. In difficult retrieval tasks, however, one needs access to a wealth of background knowledge. We present a method that uses Wikipedia-based feature generation to improve retrieval performance. Intuitively, we expect that using extensive world knowledge is likely to improve recall but may adversely affect pr...

Relevance feature mapping for content-based multimedia information retrieval

This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the...