1. A method on a first computing device for determining a set of keywords associated with a document for use as suggested search terms, comprising: receiving from a second computing device via a network a document to classify into a taxonomy, the taxonomy including a plurality of categories, each category of the plurality of categories being represented by a concatenation of a corresponding set of documents; determining a categorization ranking for each category of the plurality of categories for the received document; determining a set of categories of the taxonomy having highest categorization rankings of the plurality of categories for the received document; combining together, using one or more processors, the documents representing the categories of the determined set of categories within the taxonomy into a cumulative representative text that includes a plurality of terms; determining a term corpus importance score for each term in the cumulative representative text for each category of the set of categories based on a term frequency and an inverse category frequency; determining a cumulative term corpus importance score for each term in the cumulative representative text by combining the term corpus importance scores generated for each term for categories of the set of categories, the cumulative term corpus importance score for a particular term indicating an importance of the particular term in a context of the cumulative representative text; selecting a set of terms of the cumulative representative text having highest cumulative term corpus importance scores of the plurality of terms as the keywords, the set of terms including at least one term that is not included in the received document; and providing one or more of the keywords as one or more suggested search terms to the second computing device via the network.
1. A method on a first computing device for determining a set of keywords associated with a document for use as suggested search terms, comprising: receiving from a second computing device via a network a document to classify into a taxonomy, the taxonomy including a plurality of categories, each category of the plurality of categories being represented by a concatenation of a corresponding set of documents; determining a categorization ranking for each category of the plurality of categories for the received document; determining a set of categories of the taxonomy having highest categorization rankings of the plurality of categories for the received document; combining together, using one or more processors, the documents representing the categories of the determined set of categories within the taxonomy into a cumulative representative text that includes a plurality of terms; determining a term corpus importance score for each term in the cumulative representative text for each category of the set of categories based on a term frequency and an inverse category frequency; determining a cumulative term corpus importance score for each term in the cumulative representative text by combining the term corpus importance scores generated for each term for categories of the set of categories, the cumulative term corpus importance score for a particular term indicating an importance of the particular term in a context of the cumulative representative text; selecting a set of terms of the cumulative representative text having highest cumulative term corpus importance scores of the plurality of terms as the keywords, the set of terms including at least one term that is not included in the received document; and providing one or more of the keywords as one or more suggested search terms to the second computing device via the network. 5. The method of claim 1 , wherein at least some of the categories have one or more corresponding sub-categories, wherein said determining a cumulative term corpus importance score for each term in the cumulative representative text further comprises: for each category, counting a number of occurrences of each term in the category and any sub-categories of the category to generate a first count for each term, dividing the first count for each term by a total number of words in the category and any sub-categories of the category to generate the term frequency for each term, determining a number of categories of the taxonomy in which each term is included to generate a category number total for each term, dividing a total number of categories of the taxonomy by the category number total for each term to generate the inverse category frequency for each term, and for each term, multiplying the term frequency by the inverse category frequency to generate the term corpus importance scores.