Method and apparatus for forming a structured document from unstructured information
First Claim
Patent Images
1. A method, comprising:
- receiving, by a computer, an unstructured input document;
extracting, by the computer, a plurality of tokens from the input document, each token of the plurality of tokens having a corresponding visual style of a plurality of visual styles;
producing, by the computer for a first token of the plurality of tokens, a first probability distribution of the first token, the first probability distribution comprising a plurality of first probabilities each indicating a probability that the first token belongs to a corresponding class of a plurality of classes that are each;
related to information conveyed by the plurality of tokens; and
specific to a type of unstructured data items of the input document;
determining, by the computer from the plurality of tokens, a plurality of surrounding tokens that occur near the first token within the input document;
determining, by the computer, a first classification probability of the plurality of surrounding tokens, the first classification probability identifying the class in which the plurality of surrounding tokens are most likely to be classified;
modifying, by the computer based on the class identified by the first classification probability, each of the plurality of first probabilities to produce a corresponding second probability of a plurality of second probabilities in a second probability distribution;
producing, by the computer based on the visual style of the first token and the second probability distribution, a third probability distribution comprising a plurality of third probabilities each associated with a corresponding second probability of the plurality of second probabilities;
determining, by the computer based at least on the third probability distribution, a classification of the first token into one of the plurality of classes; and
forming, by the computer, a structured document from the first token and the classification.
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Abstract
Illustrative embodiments improve upon prior machine learning techniques by introducing an additional classification layer that mimics human visual pattern recognition. Building upon classification passes that extract contextual information, illustrative embodiments look for hints of high-level semantic categorization that manifest as visual artifacts in the document, such as font family, font weight, text color, text justification, white space, or CSS class name. An improved lightweight markup language enables display of machine-categorized tokens on a screen for human correction, thereby providing ground truths for further machine classification.
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Citations
18 Claims
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1. A method, comprising:
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receiving, by a computer, an unstructured input document; extracting, by the computer, a plurality of tokens from the input document, each token of the plurality of tokens having a corresponding visual style of a plurality of visual styles; producing, by the computer for a first token of the plurality of tokens, a first probability distribution of the first token, the first probability distribution comprising a plurality of first probabilities each indicating a probability that the first token belongs to a corresponding class of a plurality of classes that are each; related to information conveyed by the plurality of tokens; and specific to a type of unstructured data items of the input document; determining, by the computer from the plurality of tokens, a plurality of surrounding tokens that occur near the first token within the input document; determining, by the computer, a first classification probability of the plurality of surrounding tokens, the first classification probability identifying the class in which the plurality of surrounding tokens are most likely to be classified; modifying, by the computer based on the class identified by the first classification probability, each of the plurality of first probabilities to produce a corresponding second probability of a plurality of second probabilities in a second probability distribution; producing, by the computer based on the visual style of the first token and the second probability distribution, a third probability distribution comprising a plurality of third probabilities each associated with a corresponding second probability of the plurality of second probabilities; determining, by the computer based at least on the third probability distribution, a classification of the first token into one of the plurality of classes; and forming, by the computer, a structured document from the first token and the classification. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method, comprising:
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determining, by a computer, a first token of a plurality of tokens in an unstructured input document, the first token having a visual style; producing, by the computer, a first probability distribution of the first token across a plurality of classes, each class of the plurality of classes being related to a corresponding content of one or more of the plurality of tokens; modifying, by the computer, the first probability distribution to produce a second probability distribution of the first token across the plurality of classes, the second probability distribution being based on one or more classes of the plurality of classes, the one or more classes being likely to contain a plurality of surrounding tokens appearing near the first token in context of the input document; producing, by the computer, a third probability distribution of the first token across the plurality of classes, the third probability distribution being based on the visual style of the first token and the second probability distribution; determining, by the computer based at least on the third probability distribution, a classification of the first token into one of the plurality of classes; and forming, by the computer, a structured document from the first token and the classification. - View Dependent Claims (10, 11, 12, 13, 14)
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15. A device for forming a structured document from an unstructured input document, the device comprising:
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memory storing program logic; and a processor in electrical communication with the memory and executing the program logic to; extract a plurality of tokens from the input document, each token of the plurality of tokens having a visual style; produce, for each token of the plurality of tokens, a corresponding first probability distribution across a plurality of classes each being related to information conveyed by the tokens; produce, for each token of the plurality of tokens, a corresponding second probability distribution across the plurality of classes, the corresponding second probability distribution being based at least in part on the class, of the plurality of classes, in which the token'"'"'s surrounding tokens in context are most likely to be classified; and produce, for each token of the plurality of tokens, a corresponding third probability distribution across the plurality of classes, the corresponding third probability distribution being based at least in part on the corresponding visual style of the token; and classify each token of the plurality of tokens into one of the plurality of classes as a function of one or more of the first probability distribution, the second probability distribution, and the third probability distribution, wherein to classify each token, the processor executes the program logic to determine, for each class of the plurality of classes, a relative likelihood (RL) of token belonging to the class, by calculating the RL from the token'"'"'s corresponding second probability distribution for the class (C) and the token'"'"'s corresponding third probability distribution for the class (S) according to the function;
RL=C*S4. - View Dependent Claims (16, 17, 18)
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Specification