A method and system is described for modeling the content evolution of an accessed document and predicting an associated outcome for said document. The system accesses a document but can further receive additional tags, metadata, or related information that characterizes the nature of such text collection. The invention applies various processing to separate the document into elements and performs semantic modeling to create a narrative model that describes the evolution of the contents of the elements in terms of their respective sequencing. This system then uses a set of training documents with target values assigned to them to predict an associated outcome for the accessed document. The most relevant subset of a training set can be selected by matching metadata information that characterize the accessed document and a collection of metadata that characterize other broad document sets. Such characterization is done using graph partitioning or other community detection methods from metadata information that characterize the document sets and relations between multiple sets of such documents. The outcome of the method may apply to prediction of economic value of a events described by the accessed document, success measures of the document quality, or discovery of related content with similar associated outcome to the accessed document.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: accessing a document with attributes and tags that sequentially order elements of the document; extracting a selection of document text belonging to a specific set of attributes; creating a narrative model that represents evolution of semantics with respect to the sequentially ordered elements; accessing a set of target values and training documents, wherein the target value quantifies an outcome associated with one or more of the training documents in the set; and predicting an outcome associated with the accessed document. 2. The method of claim 1, wherein the semantics includes: statistical methods including one or a combination of sentiment analysis, semantic analysis, pragmatics analysis, latent class analysis, support vector machines, semantic orientation, pointwise mutual information and any document-type specific analysis. 3. The method of claim 1, further comprising: associating the training documents with communities; training a classifier between the communities and the target values; detecting relationships between the elements of the accessed document and the communities; calculating weighting based on the detected relationships, and wherein predicting the outcome is based on the classifiers and the calculated weightings. 4. The method of claim 3, wherein: obtaining a collection of topics over a corpus of documents using latent models based on the words in those documents, and using significant words in the significant topics representing a document as tags in associating documents with communities. 5. The method of claim 3, further comprising: training multiple predictors between the communities and the target values, and wherein predicting the outcome is further based on these predictors. 6. The method recited in claim 1, wherein the accessed document includes: a collection of elements arranged in its temporal succession in which elements of the documents can be accessed according to a specific set of attributes. 7. The method recited in claim 1, wherein creating the narrative model includes: creating a branching narrative that represents multiple path possibilities when applicable to the document. 8. The method of claim 1, wherein generating a prediction includes: creating narrative models for the training documents, wherein generating the prediction is further based on the narrative models for the training documents. 9. The method recited in claim 1, wherein creating the narrative model includes: generating a sequence of semantics descriptor vectors that are indexed to the sequentially ordered elements; analysing the change and association of semantics from element to element within documents with one or more additional features, tags, attributes; and representing as a collection of vectors. 10. The method recited in claim 1, wherein creating the narrative model includes: generating a contingency matrix; and using the contingency matrix in semantically analyzing the document, wherein semantically analyzing the document yields data that is inputted to the narrative model. 11. The method recited in claim 10 further comprising: training a lexicon of distributed word vectors on individual words with generative models that represent topics as frequencies of words and tracing rates of word usage with respect to the elements in the document, and wherein: generating the contingency matrix includes modifying word frequency data using the lexicon. 12. The method of claim 1, wherein predicting the outcome includes: transforming the narrative model through alignment transformation to match the number of coefficients between models with different numbers of elements. 13. The method of claim 1, wherein predicting the outcome further includes: training a classifier using the set of target values and training documents; and inputting the narrative model into the classifier wherein the classifier is used to generate the prediction. 14. The method of claim 1, wherein predicting the outcome includes: training an ensemble model that includes classifiers and/or regression models using the set of target values and training documents; and using the narrative model from the accessed document with the ensemble model to predict the associated outcome. 15. A system comprising a processor having instructions operable to cause the processor to: access a document with attributes and tags that sequentially order elements of the document; extract a selection of document text belonging to a specific set of attributes; create a narrative model that represents evolution of semantics with respect to the sequentially ordered elements; access a set of target values and training documents, wherein the target value quantifies an outcome associated with one or more of the training documents in the set; and generate a prediction of an outcome associated with the accessed document. 16. The system of claim 15, wherein the semantics includes: applying statistical methods including one or a combination of sentiment analysis, semantic analysis, pragmatics analysis, latent class analysis, support vector machines, semantic orientation, pointwise mutual information and any document-type specific analysis. 17. The system of claim 15, further comprises: associate the training documents with communities; train a classifier between the communities and the target values; detect relationships between the elements of the accessed document and the communities; calculate weighting based on the detected relationships, and wherein the prediction of the outcome is based on the classifiers and the calculated weightings. 18. The system of claim 15 being embedded in a word processing system. 19. The system of claim 15, wherein the prediction includes: metadata and factors having predictive value with respect to the outcome associated with the accessed document. 20. The system of claim 15, wherein the prediction: finds documents in a database that are closest in terms of outcome associated with the accessed document as found by the prediction method, and reports these documents as a content discovery output.