Systems and methods regarding keyword extraction

• US 8,874,568 B2
• Filed: 11/02/2011
• Issued: 10/28/2014
• Est. Priority Date: 11/05/2010
• Status: Active Grant

• Current Assignee: Rakuten Group, Inc.
• Original Assignee: Rakuten, Inc. (Rakuten Group, Inc.)
• Inventors: Stankiewicz, Zofia, Sekine, Satoshi
• Primary Examiner(s): Ahn, Sangwoo
• Application Number:
  US13/287,294
• Publication Number:
  US 20120117092A1
• Time in Patent Office: 1,091 Days
• US Class Current: 707/728
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/3334 Selection or weighting of terms from queries, including natural language queries
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 16/353 into predefined classes

Abstract

One exemplary aspect comprises a computer system comprising: (a) a preprocessing unit that extracts text from a webpage to produce at least a first set of candidate keywords, applies language processing to produce at least a second set of candidate keywords, and combines said first and second sets of candidate keywords into a first candidate pool; (b) a candidate extraction unit that receives data from said preprocessing unit describing at least said first candidate pool and produces a second candidate pool; (c) a feature extraction unit that receives data describing at least said second candidate pool and analyzes said second candidate pool for general features and linguistic features; and (d) a classification unit that receives said data describing at least said second candidate pool and related data from said feature extraction unit, and determines a likelihood of each candidate in said second candidate pool being a primary or secondary keyword.

Claims

1. A computer system comprising one or more processors that function as:

(a) a preprocessing unit that extracts text from a webpage to produce at least a first set of candidate keywords, applies language processing to produce at least a second set of candidate keywords, and combines said first and second sets of candidate keywords into a first candidate pool;

(b) a candidate extraction unit that receives data from said preprocessing unit describing at least said first candidate pool and produces a second candidate pool;

(c) a feature extraction unit that receives data describing at least said second candidate pool and analyzes said second candidate pool for general features and linguistic features, wherein said general features include number of times a term appears in the text extracted from the webpage; and

(d) a classification unit that receives said data describing at least said second candidate pool and related data from said feature extraction unit, and determines a likelihood of each candidate in said second candidate pool being a primary or secondary keyword.

2. A computer system as in claim 1, wherein at least part of said language processing is performed by a tokenizer and a syntax parser.

3. A computer system as in claim 1, wherein at least part of said language processing is performed by a tokenizer, a syntax parser, a part of speech tagger, and a named entity tagger.

4. A computer system as in claim 1, wherein at least part of said language processing is performed by a tokenizer.

5. A computer system as in claim 1, wherein at least part of said language processing is performed by a syntax parser.

6. A computer system as in claim 1, wherein at least part of said language processing is performed by a part of speech tagger.

7. A computer system as in claim 1, wherein at least part of said language processing is performed by a named entity tagger.

8. A computer system as in claim 1, wherein said first set of candidate keywords comprises metadata text.

9. A computer system as in claim 1, wherein said second candidate pool comprises noun phrases and noun sequences.

10. A computer system as in claim 1, wherein said second candidate pool comprises noun phrases, noun sequences, and n-grams.

11. A computer system as in claim 1, wherein said general features comprise one or more of frequency, position in the document, and capitalization.

12. A computer system as in claim 1, wherein said linguistic features relate to one or more of part of speech, phrase structure, and named entity information.

13. A computer system as in claim 1, wherein said general features comprise frequency features, and said frequency features comprise one or more of relative term frequency within said webpage and log of term frequency.

14. A computer system as in claim 1, wherein said determination of likelihood of each candidate being a primary or secondary keyword is based on annotated training data.

15. A computer system as in claim 1, wherein said determination of likelihood of each candidate being a primary or secondary keyword is based on training data created by combining annotation input from multiple annotators, and wherein each annotation includes a distinction between primary and secondary keywords.

16. A computer system as in claim 1, wherein said general features comprise frequency, position in the document, and capitalization, and said linguistic features relate to part of speech, phrase structure, and named entity information.

17. A computer system as in claim 1, wherein said general features comprise frequency features, said frequency features comprise one or more of relative term frequency within said webpage and log of term frequency, and said linguistic features relate to part of speech, phrase structure, and named entity information.

18. A method comprising steps implemented by a computer processing system, said steps comprising:

(a) extracting text from a webpage to produce at least a first set of candidate keywords, applying language processing to produce at least a second set of candidate keywords, and combining said first and second sets of candidate keywords into a first candidate pool;

(b) receiving data describing at least said first candidate pool and producing a second candidate pool;

(c) receiving data describing at least said second candidate pool and analyzing said second candidate pool for general features and linguistic features, wherein said general features include number of times a term appears in the text extracted from the webpage; and

(d) receiving said data describing at least said second candidate pool and related data from analyzing said second candidate pool, and determining a likelihood of each candidate in said second candidate pool being a primary or secondary keyword.

19. A non-transitory computer readable medium storing software instructions comprising:

(a) extracting text from a webpage to produce at least a first set of candidate keywords, applying language processing to produce at least a second set of candidate keywords, and combining said first and second sets of candidate keywords into a first candidate pool;

(b) receiving data describing at least said first candidate pool and producing a second candidate pool;

(c) receiving data describing at least said second candidate pool and analyzing said second candidate pool for general features and linguistic features, wherein said general features include number of times a term appears in the text extracted from the webpage; and

(d) receiving said data describing at least said second candidate pool and related data from analyzing said second candidate pool, and determining a likelihood of each candidate in said second candidate pool being a primary or secondary keyword.