Abstract: The present invention first develops a novel dualized information theory where both troenpy can quantifying the certainty while entropy can measure the uncertainty. The invention then develops a set of weighting methods for a term using the introduced dual metrics, and these methods makes use of the label information of the documents in the underlying corpus. The invention further proposes a set of class information bias features for each term using the dualized information metric. For various information retrieval and machine learning tasks the invention proposes using these combined features for optimal document representation.

Abstract: The present invention first proposes a novel expression for pairwise positive document weighting scheme and defines its symmetric dual, negative document frequency weight scheme. Their relation equations are derived and their global normalized forms are also provided. Their combination positive negative document scheme is also defined, which can quantify the features' capability of measuring the commonness of documents as well as the features' capability of distinguishing documents. The invention further proposes another form for positive document frequency via applying the strict proper score algorithm and its dual form for negative document frequency is also derived. The invention also defines the binary term frequency and its associated various document representation methods when combined with different weight schemes. The extension from common discrete token features to slightly complex features such as sentences are also presented for the above schemes and term frequencies.