Text Retrieval based on Least Information Measurement
Weimao Ke · 2017
We developed a new information retrieval framework based on the Least Information (LI) metric. We derived multiple term weighting schemes and combined them with a vector space representation for ad hoc retrieval. Given probability distributions in a collection as prior knowledge, LI Binary (LIB) quantifies least information due to the binary occurrence of a term in a document whereas LI Frequency (LIF) measures least information based on the probability of drawing a term from a bag of words. Experiments on four benchmark TREC collections for ad hoc retrieval showed that LIT-based methods achieved superior performances compared to classic TF*IDF and BM25, especially for verbose queries and hard search topics. The least information theory is a method for entropy-based information measurement and offers a novel approach for IR modeling.