CA2593378A1 - Local item extraction - Google Patents
Local item extraction Download PDFInfo
- Publication number
- CA2593378A1 CA2593378A1 CA002593378A CA2593378A CA2593378A1 CA 2593378 A1 CA2593378 A1 CA 2593378A1 CA 002593378 A CA002593378 A CA 002593378A CA 2593378 A CA2593378 A CA 2593378A CA 2593378 A1 CA2593378 A1 CA 2593378A1
- Authority
- CA
- Canada
- Prior art keywords
- address
- terms
- title
- business information
- probability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/31—Indexing; Data structures therefor; Storage structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
- G06F40/295—Named entity recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
Abstract
A system identifies a document that includes an address and locates business information in the document. The system assigns a confidence score to the business information, where the confidence score relates to a probability that the business information is associated with the address. The system determines whether to associate the business information with the address based on the assigned confidence score.
Claims (42)
1. A method, comprising:
identifying a document that includes an address;
locating business information in the document;
assigning a confidence score to the business information, the confidence score relating to a probability that the business information is associated with the address; and determining whether to associate the business information with the address based on the assigned confidence score.
identifying a document that includes an address;
locating business information in the document;
assigning a confidence score to the business information, the confidence score relating to a probability that the business information is associated with the address; and determining whether to associate the business information with the address based on the assigned confidence score.
2. The method of claim 1, wherein the business information corresponds to a title; and wherein locating business information in the document includes:
analyzing a plurality of terms that precede the address in the document, determining a probability that each of the terms is part of a title associated with the address, and identifying a candidate title based on one or more of the terms that have a high probability of being part of a title associated with the address.
analyzing a plurality of terms that precede the address in the document, determining a probability that each of the terms is part of a title associated with the address, and identifying a candidate title based on one or more of the terms that have a high probability of being part of a title associated with the address.
3. The method of claim 2, wherein the plurality of terms include a first term that immediately precedes the address in the document and one or more second terms that precede the first term in the document.
4. The method of claim 2, wherein determining a probability that each of the terms is included in a title includes:
predicting whether one of the terms is part of the title, and predicting whether another one of the terms is part of the title based on the prediction regarding the one of the terms.
predicting whether one of the terms is part of the title, and predicting whether another one of the terms is part of the title based on the prediction regarding the one of the terms.
5. The method of claim 2, wherein the probability that one of the terms is included in a title is based on a window of terms around the term.
6. The method of claim 2, wherein the probability that one of the terms is included in a title is based on the probability associated with another one of the terms.
7. The method of claim 2, wherein the probability that one of the terms is included in a title is based on a set of features associated with the term.
8. The method of claim 7, wherein the set of features includes at least one of a distance of the term from the address, characteristics of the term, boundary information between the term and a preceding or following term, or punctuation information between the term and a preceding or following term.
9. The method of claim 2, wherein the probability that one of the terms is included in a title is determined from a statistical model generated by analyzing features associated with a plurality of documents with known addresses and associated titles.
10. The method of claim 1, wherein the business information corresponds to a title; and wherein locating business information in the document includes:
analyzing a plurality of terms that precede the address in the document, determining a probability that each of the terms is part of a title associated with the address, and identifying a plurality of candidate titles based on one or more groups of the terms that have a high probability of being part of a title associated with the address.
analyzing a plurality of terms that precede the address in the document, determining a probability that each of the terms is part of a title associated with the address, and identifying a plurality of candidate titles based on one or more groups of the terms that have a high probability of being part of a title associated with the address.
H. The method of claim 1, wherein the business information corresponds to a telephone number; and wherein locating business information in the document includes:
identifying a set of candidate telephone numbers in the document, and determining a probability that each candidate telephone number in the set of candidate telephone numbers is associated with the address.
identifying a set of candidate telephone numbers in the document, and determining a probability that each candidate telephone number in the set of candidate telephone numbers is associated with the address.
12. The method of claim 11, wherein the probability that one of the candidate telephone numbers is associated with the address is based on a set of features associated with the candidate telephone number.
13. The method of claim 12, wherein the set of features includes at least one of a distance of the candidate telephone number from the address, boundary information between the candidate telephone number and the address, whether a common telephone number term appears before the candidate telephone number, whether a common facsimile number term appears before the candidate telephone number, or whether another candidate telephone number exists between the candidate telephone number and the address.
14. The method of claim 12, wherein the probability that one of the candidate telephone numbers is associated with the address is determined from a statistical model generated by analyzing features associated with a plurality of documents with known addresses and associated telephone numbers.
15. The method of claim 1, wherein the probability that the business information is associated with the address is determined from a statistical model generated by analyzing features associated with a plurality of documents with known addresses and associated business information.
16. The method of claim 1, wherein the business information includes at least one of a title, a telephone number, business hours, or a link to a web site or map associated with the address.
17. The method of claim 1, wherein determining whether to associate the business information with the address includes:
analyzing strings of terms in the document, and determining one of the strings that maximizes a probability that the terms of the string include the business information.
analyzing strings of terms in the document, and determining one of the strings that maximizes a probability that the terms of the string include the business information.
18. The method of claim 1, further comprising:
creating or supplementing a business listing based on the business information and the address when the business information is associated with the address.
creating or supplementing a business listing based on the business information and the address when the business information is associated with the address.
19. A system, comprising:
means for identifying a document that includes an address;
means for locating one or more business information candidates in the document;
means for assigning a confidence score to each of the one or more business information candidates, the confidence score associated with one of the business information candidates relating to a probability that the business information candidate is associated with the address; and means for determining whether to associate one of the one or more business information candidates with the address based on the assigned confidence score.
means for identifying a document that includes an address;
means for locating one or more business information candidates in the document;
means for assigning a confidence score to each of the one or more business information candidates, the confidence score associated with one of the business information candidates relating to a probability that the business information candidate is associated with the address; and means for determining whether to associate one of the one or more business information candidates with the address based on the assigned confidence score.
20. A system, comprising:
a memory to store a statistical model; and a processor, connected to the memory, to:
identify a document that includes an address, identify business information in the document, predict whether the business information is associated with the address based on the statistical model, and determine whether to associate the business information with the address based on the prediction.
a memory to store a statistical model; and a processor, connected to the memory, to:
identify a document that includes an address, identify business information in the document, predict whether the business information is associated with the address based on the statistical model, and determine whether to associate the business information with the address based on the prediction.
21. The system of claim 20, wherein the business information corresponds to a title; and wherein when identifying business information in the document, the processor is configured to:
analyze a plurality of terms that precede the address in the document, determine a probability that each of the terms is part of a title associated with the address based on the statistical model, and identify a candidate title based on one or more of the terms that have a high probability of being part of a title associated with the address.
analyze a plurality of terms that precede the address in the document, determine a probability that each of the terms is part of a title associated with the address based on the statistical model, and identify a candidate title based on one or more of the terms that have a high probability of being part of a title associated with the address.
22. The system of claim 21, wherein the plurality of terms includes a first term that immediately precedes the address in the document and one or more second terms that precede the first term in the document.
23. The system of claim 21, wherein when determining a probability that each of the terms is included in a title, the processor is configured to:
predict whether one of the terms is part of the title, and predict whether another one of the terms is part of the title based on the prediction regarding the one of the terms.
predict whether one of the terms is part of the title, and predict whether another one of the terms is part of the title based on the prediction regarding the one of the terms.
24. The system of claim 21, wherein the probability that one of the terms is included in a title is based on a window of terms around the term.
25. The system of claim 21, wherein the probability that one of the terms is included in a title is based on the probability associated with another one of the terms.
26. The system of claim 21, wherein the probability that one of the terms is included in a title is based on a set of features associated with the term.
27. The system of claim 26, wherein the set of features includes at least one of a distance of the term from the address, characteristics of the term, boundary information between the term and a preceding or following term, or punctuation information between the term and a preceding or following term.
28. The system of claim 20, wherein the statistical model is generated by analyzing features associated with a plurality of documents with known addresses and associated titles.
29. The system of claim 20, wherein the business information corresponds to a title; and wherein when identifying business information in the document, the processor is configured to:
analyze a plurality of terms that precedes the address in the document, determine a probability that each of the terms is part of a title associated with the address, and identify a plurality of candidate titles based on one or more groups of the terms that have a high probability of being part of a title associated with the address.
analyze a plurality of terms that precedes the address in the document, determine a probability that each of the terms is part of a title associated with the address, and identify a plurality of candidate titles based on one or more groups of the terms that have a high probability of being part of a title associated with the address.
30. The system of claim 20, wherein the business information corresponds to a telephone number; and wherein when identifying business information in the document, the processor is configured to:
identify a set of candidate telephone numbers in the document, and determine a probability that each candidate telephone number in the set of candidate telephone numbers is associated with the address based on the statistical model.
identify a set of candidate telephone numbers in the document, and determine a probability that each candidate telephone number in the set of candidate telephone numbers is associated with the address based on the statistical model.
31. The system of claim 30, wherein the probability that one of the candidate telephone numbers is associated with the address is based on a set of features associated with the candidate telephone number.
32. The system of claim 31, wherein the set of features includes at least one of a distance of the candidate telephone number from the address, boundary information between the candidate telephone number and the address, whether a common telephone number term appears before the candidate telephone number, whether a common facsimile number term appears before the candidate telephone number, or whether another candidate telephone number exists between the candidate telephone number and the address.
33. The system of claim 31 wherein the statistical model is generated by analyzing features associated with a plurality of documents with known addresses and associated telephone numbers.
34. The system of claim 20, wherein the statistical model is generated by analyzing features associated with a plurality of documents with known addresses and associated business information.
35. The system of claim 20, wherein the business information includes at least one of a title, a telephone number, business hours, or a link to a web site or map associated with the address.
36. The system of claim 20, wherein when determining whether to associate the business information with the address, the processor is configured to:
analyze strings of terms in the document, and determine one of the strings that maximizes a probability that the terms of the string include the business information.
analyze strings of terms in the document, and determine one of the strings that maximizes a probability that the terms of the string include the business information.
37. The system of claim 20, wherein the processor is further configured to create or supplement a business listing based on the business information and the address when the business information is associated with the address.
38. A method, comprising:
identifying a document that includes an address;
identifying a plurality of terms that precede the address in the document;
determining a probability that each of the terms is part of a title associated with the address;
identifying a candidate title based on one or more of the terms that have a high probability of being part of a title associated with the address;
assigning a confidence score to the candidate title; and determining whether to associate the candidate title with the address based on the assigned confidence score.
identifying a document that includes an address;
identifying a plurality of terms that precede the address in the document;
determining a probability that each of the terms is part of a title associated with the address;
identifying a candidate title based on one or more of the terms that have a high probability of being part of a title associated with the address;
assigning a confidence score to the candidate title; and determining whether to associate the candidate title with the address based on the assigned confidence score.
39. A method, comprising:
identifying a document that includes an address;
identifying a set of candidate telephone numbers in the document;
determining a probability that each candidate telephone number in the set of candidate telephone numbers is associated with the address; and determining whether to associate one of the candidate telephone numbers with the address based on the determined probability.
identifying a document that includes an address;
identifying a set of candidate telephone numbers in the document;
determining a probability that each candidate telephone number in the set of candidate telephone numbers is associated with the address; and determining whether to associate one of the candidate telephone numbers with the address based on the determined probability.
40. A method, comprising:
identifying a web page that includes a landmark;
identifying an attribute in the web page;
assigning a confidence score to the attribute, the confidence score relating to a probability that the attribute is associated with the landmark; and determining whether to associate the attribute with the landmark based on the assigned confidence score.
identifying a web page that includes a landmark;
identifying an attribute in the web page;
assigning a confidence score to the attribute, the confidence score relating to a probability that the attribute is associated with the landmark; and determining whether to associate the attribute with the landmark based on the assigned confidence score.
41. The method of claim 40, wherein the landmark corresponds to a postal address and the attribute corresponds to information relating to one of a title, a telephone number, business hours, or a link to a web site or map associated with the postal address.
42. The method of claim 40, wherein the landmark corresponds to a product and the attribute corresponds to one of a price or a product identification number.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US11/024,765 | 2004-12-30 | ||
| US11/024,765 US7831438B2 (en) | 2004-12-30 | 2004-12-30 | Local item extraction |
| PCT/US2005/047391 WO2006074052A1 (en) | 2004-12-30 | 2005-12-30 | Local item extraction |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2593378A1 true CA2593378A1 (en) | 2006-07-13 |
| CA2593378C CA2593378C (en) | 2012-06-05 |
Family
ID=36218348
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2593378A Expired - Fee Related CA2593378C (en) | 2004-12-30 | 2005-12-30 | Local item extraction |
Country Status (8)
| Country | Link |
|---|---|
| US (2) | US7831438B2 (en) |
| EP (2) | EP1839211A1 (en) |
| JP (2) | JP2008527502A (en) |
| KR (1) | KR100974905B1 (en) |
| CN (1) | CN101128819B (en) |
| AU (1) | AU2005322850C1 (en) |
| CA (1) | CA2593378C (en) |
| WO (1) | WO2006074052A1 (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7831438B2 (en) * | 2004-12-30 | 2010-11-09 | Google Inc. | Local item extraction |
| US8731954B2 (en) | 2006-03-27 | 2014-05-20 | A-Life Medical, Llc | Auditing the coding and abstracting of documents |
| US8682823B2 (en) | 2007-04-13 | 2014-03-25 | A-Life Medical, Llc | Multi-magnitudinal vectors with resolution based on source vector features |
| US7908552B2 (en) | 2007-04-13 | 2011-03-15 | A-Life Medical Inc. | Mere-parsing with boundary and semantic driven scoping |
| WO2008129339A1 (en) * | 2007-04-18 | 2008-10-30 | Mitsco - Seekport Fz-Llc | Method for location identification in web pages and location-based ranking of internet search results |
| US9946846B2 (en) | 2007-08-03 | 2018-04-17 | A-Life Medical, Llc | Visualizing the documentation and coding of surgical procedures |
| US20090182759A1 (en) * | 2008-01-11 | 2009-07-16 | Yahoo! Inc. | Extracting entities from a web page |
| US8812362B2 (en) * | 2009-02-20 | 2014-08-19 | Yahoo! Inc. | Method and system for quantifying user interactions with web advertisements |
| US8468144B2 (en) * | 2010-03-19 | 2013-06-18 | Honeywell International Inc. | Methods and apparatus for analyzing information to identify entities of significance |
| US10541053B2 (en) | 2013-09-05 | 2020-01-21 | Optum360, LLCq | Automated clinical indicator recognition with natural language processing |
| US10133727B2 (en) | 2013-10-01 | 2018-11-20 | A-Life Medical, Llc | Ontologically driven procedure coding |
| US9317873B2 (en) | 2014-03-28 | 2016-04-19 | Google Inc. | Automatic verification of advertiser identifier in advertisements |
| US20150287099A1 (en) | 2014-04-07 | 2015-10-08 | Google Inc. | Method to compute the prominence score to phone numbers on web pages and automatically annotate/attach it to ads |
| US11115529B2 (en) | 2014-04-07 | 2021-09-07 | Google Llc | System and method for providing and managing third party content with call functionality |
| US10469424B2 (en) | 2016-10-07 | 2019-11-05 | Google Llc | Network based data traffic latency reduction |
| CN109933785B (en) * | 2019-02-03 | 2023-06-20 | 北京百度网讯科技有限公司 | Method, apparatus, device and medium for entity association |
Family Cites Families (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6701307B2 (en) | 1998-10-28 | 2004-03-02 | Microsoft Corporation | Method and apparatus of expanding web searching capabilities |
| US6374241B1 (en) * | 1999-03-31 | 2002-04-16 | Verizon Laboratories Inc. | Data merging techniques |
| EP1269357A4 (en) | 2000-02-22 | 2005-10-12 | Metacarta Inc | Spatially coding and displaying information |
| US20020156779A1 (en) | 2001-09-28 | 2002-10-24 | Elliott Margaret E. | Internet search engine |
| US6965900B2 (en) | 2001-12-19 | 2005-11-15 | X-Labs Holdings, Llc | Method and apparatus for electronically extracting application specific multidimensional information from documents selected from a set of documents electronically extracted from a library of electronically searchable documents |
| JP4005477B2 (en) | 2002-05-15 | 2007-11-07 | 日本電信電話株式会社 | Named entity extraction apparatus and method, and numbered entity extraction program |
| EP1540524A2 (en) | 2002-08-05 | 2005-06-15 | Metacarta, Inc. | Desktop client interaction with a geographic text search system |
| CA2519236A1 (en) | 2003-03-18 | 2004-09-30 | Metacarta, Inc. | Corpus clustering, confidence refinement, and ranking for geographic text search and information retrieval |
| CN1536483A (en) * | 2003-04-04 | 2004-10-13 | 陈文中 | Method for extracting and processing network information and its system |
| US8346770B2 (en) * | 2003-09-22 | 2013-01-01 | Google Inc. | Systems and methods for clustering search results |
| US7349901B2 (en) * | 2004-05-21 | 2008-03-25 | Microsoft Corporation | Search engine spam detection using external data |
| US7831438B2 (en) * | 2004-12-30 | 2010-11-09 | Google Inc. | Local item extraction |
| CA2702439C (en) * | 2006-12-20 | 2017-01-31 | Victor David Uy | Method and apparatus for scoring electronic documents |
| US7877385B2 (en) * | 2007-09-21 | 2011-01-25 | Microsoft Corporation | Information retrieval using query-document pair information |
| WO2010141799A2 (en) * | 2009-06-05 | 2010-12-09 | West Services Inc. | Feature engineering and user behavior analysis |
-
2004
- 2004-12-30 US US11/024,765 patent/US7831438B2/en not_active Expired - Fee Related
-
2005
- 2005-12-30 EP EP05855882A patent/EP1839211A1/en not_active Ceased
- 2005-12-30 AU AU2005322850A patent/AU2005322850C1/en not_active Ceased
- 2005-12-30 KR KR1020077017429A patent/KR100974905B1/en not_active IP Right Cessation
- 2005-12-30 JP JP2007549625A patent/JP2008527502A/en active Pending
- 2005-12-30 WO PCT/US2005/047391 patent/WO2006074052A1/en active Application Filing
- 2005-12-30 CA CA2593378A patent/CA2593378C/en not_active Expired - Fee Related
- 2005-12-30 EP EP11163711A patent/EP2372584A1/en not_active Withdrawn
- 2005-12-30 CN CN2005800486396A patent/CN101128819B/en not_active Expired - Fee Related
-
2010
- 2010-09-23 US US12/888,925 patent/US8433704B2/en active Active
-
2011
- 2011-03-04 JP JP2011047519A patent/JP5226095B2/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| US7831438B2 (en) | 2010-11-09 |
| AU2005322850A1 (en) | 2006-07-13 |
| US20110047151A1 (en) | 2011-02-24 |
| EP2372584A1 (en) | 2011-10-05 |
| CA2593378C (en) | 2012-06-05 |
| CN101128819B (en) | 2011-06-22 |
| US8433704B2 (en) | 2013-04-30 |
| AU2005322850B2 (en) | 2010-02-11 |
| KR100974905B1 (en) | 2010-08-09 |
| JP2011129154A (en) | 2011-06-30 |
| AU2005322850C1 (en) | 2010-07-15 |
| US20060149565A1 (en) | 2006-07-06 |
| CN101128819A (en) | 2008-02-20 |
| JP5226095B2 (en) | 2013-07-03 |
| EP1839211A1 (en) | 2007-10-03 |
| WO2006074052A1 (en) | 2006-07-13 |
| KR20070092755A (en) | 2007-09-13 |
| JP2008527502A (en) | 2008-07-24 |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| EEER | Examination request | ||
| MKLA | Lapsed |
Effective date: 20161230 |