Syracuse University

Textwise LLC

Other

Technology Innovations, LLC

Elizabeth D Liddy

Woojin Paik

Mary E McKenna

Eileen E Allen

Edmund S Yu

Jennifer Heverin Liddy

Ming Li

Ian Harcourt Niles

Michael L Weiner

David L Snyder

Theodore G Diamond

Bhaskaran Balakrishnan

Edmund Szu-Li Yu

Edmund Szu-li Yu

Techniques for controlling distribution of information from a secure domain by automatically detecting outgoing messages which violate security policies corresponding to the secure domain. Semantic models are constructed for one or more message categories and for the outgoing messages. The semantic model of an outgoing message is compared with the semantic models of the message categories to determine a degree of similarity between the semantic models. The outgoing message is classified based on the degree of similarity obtained from the comparison. A determination is made, based on the classification of the outgoing message, if distribution of the outgoing message would violate a security policy for the secure domain. Distribution of the outgoing message is allowed if no security policy is violated.

Techniques for controlling distribution of information from a secure domain

A system for retrieving multimedia information is provided using a computer coupled to a computer-based network, such as the Internet, and particularly the World Wide Web (WWW). The system includes a web browser, a graphic user interface enabled through the web browser to allow a user to input a query representing the information the user wishes to retrieve, and an agent server for producing, training, and evolving first agents and second agents. Each of the first agents retrieves documents (Web page) from the network at a different first network address and at other addresses linked from the document at the first network address. Each of the second agents executes a search on different search engines on the network in accordance with the query to retrieve documents at network addresses provided by the search engine. The system includes a natural language processor which determines the subject categories and important terms of the query, and of the text of each agent retrieved document. The agent server generates and trains an artificial neural network in accordance with the natural language processed query, and embeds the trained artificial neural network in each of the first and second agents. During the search, the first and second agents process through their artificial neural network the subject categories and important terms of each document they retrieve to determine a retrieval value for the document. The graphic user interface displays to the user the addresses of the retrieved documents which are above a threshold retrieval value. The user manually, or the agent server automatically, selects which of the retrieved documents are relevant. Periodically, the artificial neural network of the first and second agents is expanded and retrained by the agent server in accordance with the selected relevant documents to improve their ability to retrieve documents which may be relevant to the query. Further, the agent server can evolve an artificial neural network based on the current artificial neural network, the retrieved documents, and their selected relevancy, by iteratively producing, training, and testing several generations of neural networks to produce an evolved agent. The artificial neural network of the evolved agent then replaces the current artificial neural network used by the agents to search the Internet. One or more concurrent search of the Internet may be provided.

System for retrieving multimedia information from the internet using multiple evolving intelligent agents

An information extraction system that allows users to ask questions about documents in a database, and responds to queries by returning possibly relevant information which is extracted from the documents. The system is domain-independent, and automatically builds its own subject knowledge base. It can be applied to any new corpus of text with quick results, and no requirement for lengthy manual input. For this reason, it is also a dynamic system which can acquire new knowledge and add it to the knowledge base immediately by automatically identifying new names, events, or concepts.

Information extraction system and method using concept-relation-concept (CRC) triples

Information extraction system and method using concept relation concept

Techniques for generating sophisticated representations of the contents of both queries and documents in a retrieval system by using natural language processing (NLP) techniques to represent, index, and retrieve texts at the multiple levels (e.g., the morphological, lexical, syntactic, semantic, discourse, and pragmatic levels) at which humans construe meaning in writing. The user enters a query and the system processes the query to generate an alternative representation, which includes conceptual-level abstraction and representations based on complex nominals (CNs), proper nouns (PNs), single terms, text structure, and logical make-up of the query, including mandatory terms. After processing the query, the system displays query information to the user, indicating the system's interpretation and representation of the content of the query. The user is then given an opportunity to provide input, in response to which the system modifies the alternative representation of the query. Once the user has provided desired input, the possibly modified representation of the query is matched to the relevant document database, and measures of relevance generated for the documents. A set of documents is presented to the user, who is given an opportunity to select some or all of the documents, typically on the basis of such documents being of particular relevance. The user then initiates the generation of a query representation based on the alternative representations of the selected document(s).

User interface and other enhancements for natural language information

A document retrieval system where a user can enter a query, including a natural language query, in a desired one of a plurality of supported languages, and retrieve documents from a database that includes documents in at least one other language of the plurality of supported languages. The user need not have any knowledge of the other languages. Each document in the database is subjected to a set of processing steps to generate a language-independent conceptual representation of the subject content of the document. This is normally done before the query is entered. The query is also subjected to a (possibly different) set of processing steps to generate a language-independent conceptual representation of the subject content of the query. The documents and queries can also be subjected to additional analysis to provide additional term-based representations, such as the extraction of information-rich terms and phrases (such as proper nouns). Documents are matched to queries based on the conceptual-level contents of the document and query, and, optionally, on the basis of the term-based representation. The query's representation is then compared to each document's representation to generate a measure of relevance of the document to the query.

Multilingual document retrieval system and method using semantic vector

Natural language information retrieval system and method

A natural language processing system uses unformatted naturally occurring text and generates a subject vector representation of the text, which may be an entire document or a part thereof such as its title, a paragraph, clause, or a sentence therein. The subject codes which are used are obtained from a lexical database and the subject code(s) for each word in the text is looked up and assigned from the database. The database may be a dictionary or other word resource which has a semantic classification scheme as designators of subject domains. Various meanings or senses of a word may have assigned thereto multiple, different subject codes and psycholinguistically justified sense meaning disambiguation is used to select the most appropriate subject field code. Preferably, an ordered set of sentence level heuristics is used which is based on the statistical probability or likelihood of one of the plurality of codes being the most appropriate one of the plurality. The subject codes produce a weighted, fixed-length vector (regardless of the length of the document) which represents the semantic content thereof and may be used for various purposes such as information retrieval, categorization of texts, machine translation, document detection, question answering, and generally for extracting knowledge from the document. The system has particular utility in classifying documents by their general subject matter and retrieving documents relevant to a query.

Natural language processing system for semantic vector representation

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Syracuse, NY, United States of America

Elizabeth D Liddy