NATURAL LANGUAGE SEARCH SYSTEM

• US 20070106499A1
• Filed: 08/08/2006
• Published: 05/10/2007
• Est. Priority Date: 08/09/2005
• Status: Abandoned Application

• Current Assignee: Cognition Technologies, Inc. (Microsoft Corporation)
• Original Assignee: Cognition Technologies, Inc. (Microsoft Corporation)
• Inventors: Wallace, Karen, Deane, Paul, Dahlgren, Kathleen, Stabler, Edward
• Application Number:
  US11/463,296
• Publication Number:
  US 20070106499A1
• Time in Patent Office: Days
• US Class Current: 704/10
• CPC Class Codes: G06F 16/243 Natural language query formulation
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/36 Creation of semantic tools, e.g. ontology or thesauri

Abstract

A natural language system searching system develops concept and string indexes of a textual database, such as a group of litigation documents, by breaking the text to be indexed into sentences, words, dates, names and places in a reader, identifying phrases in a phrase parser, recovering word stems in a morphology module and determining the sense of potentially ambiguous words in a sense selector, all in accordance with words and concepts (word senses) stored in lexicon database 9-32. A query may then be processed by the reader, phrase parser, morphology module, and sense selector to provide a text meaning output which can be compared with the concept and string indexes to identify, retrieve and display documents and/or portions of documents related to the query. A lexicon enhancer adds vocabulary semi-automatically.

Claims

1. A method of searching a collection a database of documents, comprising:

providing a natural language understanding (NLU) module which parses text and disambiguates the parsed text using a naive semantic lexicon providing an ontology aspect to classify concepts and a descriptive aspect to identify properties of the concept;

processing documents in a database with the NLU module to generate cognitive models of each of documents and a searchable index of the cognitive models in a predetermined format indicating the possible, non-ambiguous meanings of the concepts together with synonyms and hypemyms of the concepts by selection from a precompiled static dictionary and ontology database;

processing a query with the NLU module to generate a cognitive model of the query in the predetermined format without synonyms and hypernyms;

comparing the cognitive model of the query with the searchable index to select the documents likely to be relevant to the query; and

comparing the cognitive model of the query with the full text of the selected documents to select the documents to include in a response to the query.