The invention relates generally to natural language processing systems and information extraction processes that involve the extraction of information from source documents. An information extraction process should be distinguished from two other natural language processes: text or informational retrieval processes and text understanding processes. Text or informational retrieval processes typically identify documents from a library of documents by matching of words or phrases contained within the documents. Text understanding processes aim to interpret the complete meaning of entire texts, including the text's subtle nuances of meaning and complexities of language.
Traditional information extraction processes are usually implemented on a programmed general purpose computer. The process looks for certain information in the text, extracts the information, and organizes the information into a database records. The database created is usually stored in a searchable format, such a structured relational database or an object-oriented structured database, which can be accessed, research, and analyzed by computer-implemented database research systems.
In "The Generic Information Extraction System", Proceedings of the Fifth Message Understanding Conference (MUC-5), 1993, by J. R. Hobbs describes a generic information extraction system in ten steps. First, a text zoner, turns a text into a set of text segments, then a pre-processor turns a text or text segment into a sequence of sentences, each of which is a sequence of lexical terms, where a lexical item is a word together with its lexical attributes. Third, a filter turns a set of sentences into a smaller set of sentences by filtering out the irrelevant sentences. Fourth, a preparser takes a sequence of lexical items and tries to identify various determinable small-scale structures. Fifth, a parser produces a set of parse tree fragments from the sequence of lexical terms and small-scale structures. Sixth, a fragment combiner combines the fragments into a parse tree or logical form. Seventh, a semantic interpreter generates a semantic structure from the parse tree or logical form. Eighth, a lexical disambiguator replaces general or ambiguous predicates in the semantic structure with specific and unambiguous predicates. Ninth, a discourse or conference resolution processor turns the tree-like semantic structure into a network like structure by identifying different descriptions of the same entity in different parts of the text. Finally, a template generator derives the output template from the final semantic structure. Accordingly in "Tasks, Domains, and Languages", Proceedings of the Fifth Message Understanding Conference (MUC-5), 1993, by B. Onyshkevych et al. the tasks requested of the information extraction systems evaluated at MUC-5 focused on the systems' ability to automatically fill one singular object-oriented template with information extracted from a source of free text.
However, the generic information extraction process described above only inputs the extracted information into the database, or template, in the last step of the process, and does not address the problem of compiling or aggregating a large and comprehensive database from a plurality of source documents.
In addition, the information extraction processes do not address the problem of compiling or aggregating information extracted from both structured and unstructured source material, i.e. free text, forms.
Furthermore, the information extraction processes are not focused on how the information extracted will be used in the construction of a large database. It would be a desirable feature to have an information extraction system with the ability to assemble extracted information and to recognize any conflicts between the extracted information and the contents of an existing database. It would also be a desirable feature for this information extraction and assemblage system, or information indexing system, to fully analyze the extracted information in a comprehensive and intelligent manner to provide a full range of options and alternatives to the user to resolve any inconsistencies between the extracted information and the database under construction.
Another desirable feature would be for the system for the information indexing system to have the capacity of maintaining conflicts and fragments of incomplete information until they are resolved at a later date.
Accordingly, an information indexing system with the above features would have the ability to construct a database with a high degree of integrity from information extracted from a plurality of source documents.
USPTO Backgrounds
USPTO Backgrounds