Information Extraction Systems, Inc.

Dictaphone Corporation.

Other

Thomas J DePlonty

Alwin B Carus

We describe here a system and method for creating, maintaining and using a semantic search engine environment for precise retrieval of curated answers to questions where the answers may be drawn from an authoritative document collection. The invention combines processing by human developers and software: semantic editing tools for creating, storing, maintaining queries and variants of queries, and query and document passage categories; links from queries to text passages that provide answers to these queries; a document retrieval store; means for matching user queries against stored queries; means for creating, storing, maintaining, and retrieving semantic and management metadata and categories about queries and documents and using these data for navigating the document collection; and means for finding information related to the user's information need by text and semantic similarity retrieval.

Apparatus, system and method for a semantic editor and search engine

The present invention relates to an apparatus system and method for creating a customizable and application-specific semantic similarity utility that uses a single similarity measuring algorithm with data from broad-coverage structured lexical knowledge bases (dictionaries and thesauri) and corpora (document collections). More specifically the invention includes the use of data from custom or application-specific structured lexical knowledge bases and corpora and semantic mappings from variant expressions to their canonical forms. The invention uses a combination of technologies to simplify the development of a generic semantic similarity utility; and minimize the effort and complexity of customizing the generic utility for a domain- or topic-dependent application. The invention makes customization modular and data-driven, allowing developers to create implementations at varying degrees of customization (e.g., generic, domain-level, company-level, application-level) and also as changes occur over time (e.g., when product and service mixes change).

Apparatus, system and method for application-specific and customizable semantic similarity measurement

The disclosed invention includes an apparatus, system and method for developing tools to explore, organize, structure, extract, and mine natural language text. The system contains three sub-systems: a run-time engine, a development environment, and a feedback system. The invention also includes a system and method for improving the quality of information extraction applications consisting of an ensemble of per-user, adaptive, on-line machine-learning classifiers that adapt to document content and judgments of users by continuously incorporating feedback from information extraction results and corrections that users apply to these results. At least one of the machine-learning classifier also provides explanations or justifications for classification decisions in the form of rules; other machine-learning classifiers may provide feedback in the form of supporting instances or patterns.

Apparatus, system and method for developing tools to process natural language text

One embodiment generally pertains to a method of prediction. The method includes generating a set of affixes from a selected input sequence and comparing the set of affixes with a predictive set of affixes. The method also includes selecting an affix from the predictive set of affixes. The invention uses various input data sets and allows the ability to perfectly render the original data set and the minimal size of the predictive set of affixes.

System, method and apparatus for prediction using minimal affix patterns

The present invention relates generally to a system and method for categorization of strings of words. More specifically, the present invention relates to a system and method for normalizing a string of words for use in a system for categorization of words in a predetermined categorization scheme. A method for adaptive categorization of words in a predetermined categorization scheme may include receiving a string of text, tagging the string of text, and normalizing the string of text. Normalization may be performed with a three-stage algorithm including a literal match processing stage, an approximation match processing stage, and a nearest neighbor match processing stage. The normalized string of text can be compared to a number of sequences of text in the predetermined categorization scheme.

System and method for normalization of a string of words

We have discovered a system and method for improving the quality of information extraction applications consisting of an ensemble of per-user, adaptive, on-line machine-learning classifiers that adapt to document content and judgments of users by continuously incorporating feedback from information extraction results and corrections that users apply to these results. The satellite classifier ensemble uses only the immediately available features for classifier improvement and it is independent of the complex cascade of earlier decisions leading to the final information extraction result. The machine-learning classifiers may also provide explanations or justifications for classification decisions in the form of rules, other machine-learning classifiers may provide feedback in the form of supporting instances or patterns. Developers control the number and type of classifiers in the satellite classifier ensemble, the pre-deployment training of these classifiers, and the features monitored by the implemented satellite classifier ensemble.

Satellite classifier ensemble

A semantic discovery and exploration system is disclosed where an environment enabling a developer or user to uncover, navigate, and organize semantic patterns and structures in a document collection with or without the aid of structured knowledge. The semantic discovery and exploration system provides techniques for searching document collections, categorizing documents, inducing lists of related concepts, and identifying clusters of related terms and documents. This system operates both without and with infusions of structured knowledge such as gazetteers, thesauruses, taxonomies and ontologies. System performance improves when structured knowledge is incorporated. The semantic discovery and exploration system may be used as a first step in developing an information extraction system such as to categorize or cluster documents in a particular domain or to develop gazetteers and as a part of a deployed run-time information extraction system. It may also be used as standalone utility for searching, navigating, and organizing document collections and structured knowledge bases such as dictionaries or domain-specific reference works.

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Melrose, MA, United States of America

Thomas J DePlonty