Decision-support expert system and methods for real-time exploitation of documents in non-english languages

A method for automatic real-time summarization and information extraction from one or more documents in a source language in order to present their content in a summary in a chosen target language and to input the content as categorized entities, the method comprising:

• a. selecting said source language;
• b. processing an input document into a textual format document while preserving visual information;
• c. performing linguistic analysis based on models of a linguistic-register associated with the analyzed document;
• d. extracting lexical instances corresponding to at least one of a pre-determined domain-specific lexicon and run-time lexical instances built according to pre-determined syntactic rules;
• e. extracting ontology elements from the lexical instances to obtain a set of ontological instances comprising instances from a pre-determined ontology and refined ontological instances, said refined ontological instances comprising a combination of pre-determined ontological instances;
• f. creating a document digest (DD) from the set of ontological instances, the DD being presented in a relationary map which retains information on features, context and linguistic origin of the components of the DD;
• g. using domain-specific statistical models relating to categorization clusters and categories within said categorization clusters to determine the most likely categorization of the document in relation to a spectrum of categories by comparing the updated DD to each category model;
• wherein the step of using domain-specific models includes:
• i. creating a vector representing each DD using a pre-categorized corpus of domain-specific documents;
• ii. analyzing those documents to extract a model of a document digest which corresponds to or contradicts each category of document in the domain;
• iii. using statistical algorithms to create the models and creating mixed models of the different results which are specific to each category;
• iv. creating a mixed model of the results of analysis of each text;
• v. calculating a similarity result between each of a plurality of statistical models obtained offline of said categories and the vector generated by the DD of the input text and determining the most likely category in each cluster;
• vi. adding ontological instances to the DD based on the input of the categorization upon which rules from the rule base are applied;
• and
• vii. analyzing the winner model to extract a confidence score related to the DD;
• h. creating in said target language a document summary for each of said one or more documents based on said DD and summarization rules.