Data classification methods using machine learning techniques
First Claim
1. A method for adapting to a shift in document content, comprising:
- receiving at least one labeled seed document;
receiving unlabeled documents;
receiving at least one predetermined cost factor;
training a transductive classifier using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents;
classifying the unlabeled documents having a confidence level above a predefined threshold into a plurality of categories using the classifier;
reclassifying at least some documents previously categorized by a different classifier into the categories using the classifier; and
outputting identifiers of the categorized documents to at least one of a user, another system, and another process.
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Accused Products
Abstract
A method for adapting to a shift in document content according to one embodiment of the present invention includes receiving at least one labeled seed document; receiving unlabeled documents; receiving at least one predetermined cost factor; training a transductive classifier using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents; classifying the unlabeled documents having a confidence level above a predefined threshold into a plurality of categories using the classifier; reclassifying at least some of the categorized documents into the categories using the classifier; and outputting identifiers of the categorized documents to at least one of a user, another system, and another process. Methods for separating documents are also presented. Methods for document searching are also presented.
118 Citations
16 Claims
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1. A method for adapting to a shift in document content, comprising:
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receiving at least one labeled seed document; receiving unlabeled documents; receiving at least one predetermined cost factor; training a transductive classifier using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents; classifying the unlabeled documents having a confidence level above a predefined threshold into a plurality of categories using the classifier; reclassifying at least some documents previously categorized by a different classifier into the categories using the classifier; and outputting identifiers of the categorized documents to at least one of a user, another system, and another process. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method for separating documents, comprising:
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receiving labeled data; receiving a sequence of unlabeled documents; adapting probabilistic classification rules using transduction based on the labeled data and the unlabeled documents; updating weights used for document separation according to the probabilistic classification rules; determining locations of separations between the documents in the sequence of documents according to said probabilistic classification rules; outputting indicators of the determined locations of the separations in the sequence to at least one of a user, another system, and another process; and flagging the documents with codes, the codes correlating to the indicators.
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8. A method for document searching, comprising:
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receiving a search query; retrieving documents based on the search query; outputting the documents; receiving user-entered labels for at least some of the documents, the labels being indicative of a relevance of the document to the search query; training a classifier based on the search query and the user-entered labels; performing a document classification technique on the documents using the classifier for reclassifying the documents; and outputting identifiers of at least some of the documents based on the classification thereof. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A method for document searching, comprising:
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receiving a search query; retrieving documents based on the search query; outputting the documents; receiving user-entered labels for at least some of the documents, the labels being indicative of a relevance of the document to the search query; training a transductive classifier based on the search query and the user-entered labels, wherein the transductive classifier is trained through iterative calculation using at least one predetermined cost factor, the search query, and the documents, wherein for each iteration of the calculations the cost factor is adjusted as a function of an expected label value, and using the trained classifier to classify the documents; performing a document classification technique on at least some of the documents using the classifier for classifying the at least some of the documents; and outputting identifiers of the at least some of the documents based on the classification thereof. - View Dependent Claims (16)
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Specification