Method of dualized information theory based Term Frequency Weighting Schemes and Features for Document Representation
OwnerArthur Jun ZHANGinventor
Life of this patent
Estimating the term…
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.
Claims11
A term weighting method leveraging document labels for a corpus of documents, comprising:
- an information quantity of the label class counts for documents with the term presence;
- and
- an information quantity of the label class counts for documents with the term absence.
A method for computing a term level class information bias feature comprise a distributed component averaging the odds-ratio of Inverse Document Frequencies of the total counts of documents for the specific class count and the count of the documents with the term presence and the class label across all the available classes.
Dependencies are inferred from the claim text.