Systems, methods, and software for hyperlinking names

• US 7,333,966 B2
• Filed: 06/13/2002
• Issued: 02/19/2008
• Est. Priority Date: 12/21/2001
• Status: Active Grant

• Current Assignee: Thomson Reuters Enterprise Centre GmbH (The Woodbridge Co. Ltd.)
• Original Assignee: Thomson Global Resources
• Inventors: Dozier, Christopher C.
• Primary Examiner(s): Knight; Anthony
• Assistant Examiner(s): Chang; Sunray
• Application Number:
  US10/171,170
• Publication Number:
  US 20030135826A1
• Time in Patent Office: 2,077 Days
• Field of Search: 709/217, 707/101, 707/3, 707/2, 707/100, 715/515, 706/21, 706/16, 706/20, 235/435
• US Class Current: 706/21
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/94 Hypermedia Hyperlinking G06F40/134
• CPC Class Codes: G06F 16/9558 Details of hyperlinks; Management of linked annotations
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing

Abstract

Hyperlinking or associating documents to other documents based on the names of people in the documents has become more desirable. Although there is an automated system for installing such hyperlinks into judicial opinions, the system is not generally applicable to other types of names and documents, nor well suited to determine hyperlinks for names that might refer to two or more similarly named persons. Accordingly, the inventor devised systems, methods, and software that facilitate hyperlinking names in documents, regardless of type. One exemplary system includes a descriptor module and a linking module. The descriptor module develops descriptive patterns for selecting co-occurent document information that is useful in recognizing associations between names and professional classes. The linking module tags names in an input document, extracts co-occurent information using the descriptive patterns, and uses a Bayesian inference network that processes a (non-inverse-document-frequency) name-rarity score for each name along with the name and selected co-occurent document information to determine appropriate hyperlinks to other documents, such as entries in professional directories.

Claims

1. A computer-implemented method comprising:

identifying one or more person names in a set of one or more documents, with each identified person name more likely to refer to a single person in a profession than other person names in the document;

identifying descriptive language from one or more documents, based on the identified names; and

identifying within one or more documents other person names that refer to persons in the profession, based on one or more portions of the identified descriptive language.

2. The method of claim 1, wherein identifying person names in a set of documents comprises:

identifying a plurality of person names in the set of documents, with each name including at least a last name;

calculating for each of the plurality of person names, a quantity based on a probability of drawing its last name at random from a set of last names in a search universe.

3. The method of claim 1, wherein calculating for each of the plurality of person names a quantity based on the probability of drawing its last name at random from a set of last names, includes:

calculating a quantity based on a size of a human population, a probability of drawing a first portion of the name at random from all the first names in a relevant search universe, and a probability of drawing a second portion of the name at random from all the last names in the search universe.

4. The method of claim 1, wherein identifying descriptive language from one or more documents, based on the identified names comprises identifying appositives related to the identified names.

5. The method of claim 1, wherein identifying descriptive language from one or more documents, based on the identified names in the set of documents comprises:

identifying a set of terms, including one or more first terms preceding one or more of the identified names and one or more second terms succeeding one or more of the identified names.

6. The method of claim 1, wherein the one or more first terms includes one or more parts of speech and one or more of the second terms includes one or more parts of speech.

7. A machine-readable medium comprising machine executable instructions for:

identifying one or more person names in a set of one or more documents, with each identified person name more likely to refer to a single person in a profession than other person names in the document;

identifying descriptive language from one or more documents, based on the identified names; and

identifying within one or more documents other person names that refer to persons in the profession, based on one or more portions of the identified descriptive language.

8. A system comprising:

at least one processor

a memory coupled to the processor, the memory including instructions for;

identifying one or more person names in a set of one or more documents, with each identified person name more likely to refer to a single person in a profession than other person names in the document;

identifying descriptive language from one or more documents, based on the identified names; and

identifying within one or more documents other person names that refer to persons in the profession regardless of their name uniqueness, based on one or more portions of the identified descriptive language.