CONDITIONAL RANDOM FIELDS (CRF)-BASED RELATION EXTRACTION SYSTEM

• US 20110035210A1
• Filed: 08/09/2010
• Published: 02/10/2011
• Est. Priority Date: 08/10/2009
• Status: Abandoned Application

• Current Assignee: Digital Trowel (Israel) Ltd.
• Original Assignee: Digital Trowel (Israel) Ltd.
• Inventors: Rosenfeld, Benjamin, Feldman, Ronen
• Application Number:
  US12/852,678
• Publication Number:
  US 20110035210A1
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/295 Named entity recognition

Abstract

A system for extracting information from text, the system including parsing functionality operative to parse a text using a grammar, the parsing functionality including named entity recognition functionality operative to recognize named entities and recognition probabilities associated therewith and relationship extraction functionality operative to utilize the named entities and the probabilities to determine relationships between the named entities, and storage functionality operative to store outputs of the parsing functionality in a database.

Claims

1. A system for extracting information from text, said system comprising:

parsing functionality operative to parse a text using a grammar, said parsing functionality including;

named entity recognition functionality operative to recognize named entities and recognition probabilities associated therewith; and

relationship extraction functionality operative to utilize said named entities and said probabilities to determine relationships between said named entities; and

storage functionality operative to store outputs of said parsing functionality in a database.

2. A system for extracting information from text, said system comprising:

parsing functionality operative to parse a text using a grammar, said grammar including a plurality of rules, at least some of said plurality of said rules having different weights assigned thereto, said parsing functionality employing said weights to select preferred results of said parsing; and

storage functionality operative to store outputs of said parsing functionality in a database.

3. A system for extracting information from text according to claim 2 and wherein said parsing functionality includes:

named entity recognition functionality operative to recognize named entities and recognition probabilities associated therewith; and

relationship extraction functionality operative to utilize said named entities and said probabilities to determine relationships between said named entities.

4. A system for extracting information from text according to claim 3 and wherein both said weights and said probabilities are employed to select preferred results of said parsing.

5. A system for extracting information from text according to claim 4 and wherein said weights are trained using a labeled corpus.

6. A system for extracting information from text according to claim 4 and wherein said weights are specified by a knowledge engineer.

7. A system for extracting information from text according to claim 2 and wherein said rules utilize results of sequence classifiers.

8. A method for extracting information from text, said method comprising:

parsing a text using a grammar, said parsing including;

recognizing named entities and recognition probabilities associated therewith; and

utilizing said named entities and said probabilities to determine relationships between said named entities; and

storing outputs of said parsing in a database.

9. A method for extracting information from text, said method comprising:

parsing a text using a grammar, said grammar including a plurality of rules, at least some of said plurality of said rules having different weights assigned thereto, said parsing employing said weights to select preferred results of said parsing; and

storing outputs of said parsing in a database.

10. A method for extracting information from text according to claim 9 and wherein said parsing includes:

recognizing named entities and recognition probabilities associated therewith; and

utilizing said named entities and said probabilities to determine relationships between said named entities.

11. A method for extracting information from text according to claim 10 and wherein both said weights and said probabilities are employed to select preferred results of said parsing.

12. A method for extracting information from text according to claim 11 and wherein said weights are trained using a labeled corpus.

13. A method for extracting information from text according to claim 11 and wherein said weights are specified by a knowledge engineer.

14. A method for extracting information from text according to claim 9 and wherein said rules utilize results of sequence classifiers.