1. A method of forming a structured document from an unstructured input document, the method comprising: receiving the input document from a data communication network; storing the received input document in a storage system; in a first computer process, extracting a plurality of textual tokens from the input document, each extracted token having a visual style; in a second computer process, applying a content classifier to the plurality of tokens to produce, for each token therein, a first probability distribution of the given token with respect to a plurality of textual classes, the first probability distribution comprising a plurality of first probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of first probabilities associated therewith, each of the plurality of textual classes being: related to information conveyed by the textual tokens; and specific to a type of unstructured data items of the input document; in a third computer process, applying a context classifier to each token to redistribute the first probability distribution of each token, based on the textual class having the highest first probability of the token's surrounding tokens in context, thereby producing a second probability distribution of the given token with respect to the plurality of textual classes, the second probability distribution comprising a plurality of second probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of second probabilities associated therewith; in a fourth computer process, applying a visual style classifier to each token based on its visual style and the second probability distribution, and not based on a corpus containing any visual style that is not found in the input document, thereby producing a third probability distribution of the given token with respect to the plurality of textual classes, the third probability distribution comprising a plurality of third probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of third probabilities associated therewith; determining a classification for each token into one of the plurality of textual classes as a function of the second and third probability distributions; and in the storage system, forming a structured document from the plurality of classified tokens.
1. A method of forming a structured document from an unstructured input document, the method comprising: receiving the input document from a data communication network; storing the received input document in a storage system; in a first computer process, extracting a plurality of textual tokens from the input document, each extracted token having a visual style; in a second computer process, applying a content classifier to the plurality of tokens to produce, for each token therein, a first probability distribution of the given token with respect to a plurality of textual classes, the first probability distribution comprising a plurality of first probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of first probabilities associated therewith, each of the plurality of textual classes being: related to information conveyed by the textual tokens; and specific to a type of unstructured data items of the input document; in a third computer process, applying a context classifier to each token to redistribute the first probability distribution of each token, based on the textual class having the highest first probability of the token's surrounding tokens in context, thereby producing a second probability distribution of the given token with respect to the plurality of textual classes, the second probability distribution comprising a plurality of second probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of second probabilities associated therewith; in a fourth computer process, applying a visual style classifier to each token based on its visual style and the second probability distribution, and not based on a corpus containing any visual style that is not found in the input document, thereby producing a third probability distribution of the given token with respect to the plurality of textual classes, the third probability distribution comprising a plurality of third probabilities each associated with a corresponding textual class of the plurality of textual classes such that each textual class has one of the plurality of third probabilities associated therewith; determining a classification for each token into one of the plurality of textual classes as a function of the second and third probability distributions; and in the storage system, forming a structured document from the plurality of classified tokens. 6. A method according to claim 1 , further comprising: displaying the tokens on a video display; receiving an indication from an individual viewing the video display that a token has been misclassified; and reclassifying the token into a different textual class according to the indication.