Query phrasification

• US 8,166,021 B1
• Filed: 03/30/2007
• Issued: 04/24/2012
• Est. Priority Date: 03/30/2007
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Cao, Pei, Mazumdar, Soham
• Primary Examiner(s): Trujillo, James
• Assistant Examiner(s): Singh, Amresh
• Application Number:
  US11/694,845
• Time in Patent Office: 1,852 Days
• Field of Search: 707/1, 707/3, 707/713, 707/748
• US Class Current: 707/713
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 16/3344 using natural language analysis
• CPC Class Codes: G06F 40/289 Phrasal analysis, e.g. finite state techniques or chunking

Abstract

An information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are extracted from the document collection. Documents are the indexed according to their included phrases, using phrase posting lists. The phrase posting lists are stored in an cluster of index servers. The phrase posting lists can be tiered into groups, and sharded into partitions. Phrases in a query are identified based on possible phrasifications. A query schedule based on the phrases is created from the phrases, and then optimized to reduce query processing and communication costs. The execution of the query schedule is managed to further reduce or eliminate query processing operations at various ones of the index servers.

Claims

1. A computer-implemented method for identifying valid phrases in an input text comprising a plurality of three or more words, the method comprising:

decomposing, by at least one processor of a computer system, the input text into a plurality of candidate phrasifications, including different groupings of words of the input text, each candidate phrasification comprising a disjoint union of component phrases, and each component phrase including at least one word or related word of the input text;

scoring, by at least one of the processors of the computer system, at least two of the candidate phrasifications, wherein the candidate phrasifications include two or more component phrases, and wherein the scoring is based on a probability of occurrence of each of the candidate phrasification'"'"'s component phrases, and is based on the number of component phrases constituting the candidate phrasification, wherein candidate phrasifications having relatively fewer component phrases are weighted higher than candidate phrasifications having relatively more component phrases;

comparing, by at least one of the processors of the computer system, a score for each scored candidate phrasification to a threshold value;

selecting, by at least one of the processors of the computer system, at least one candidate phrasification, wherein the scores of each selected phrasification exceeds a chosen threshold value; and

identifying, by at least one of the processors of the computer system, the component phrases of each selected phrasification as valid phrases for the input text.

2. The method of claim 1, wherein decomposing the input text comprises:

(i) restructuring a word tree derived from the input text, where the word tree comprises leaf nodes that are words and non-leaf nodes that are Boolean operators AND, OR, or NOT; and

(ii) generating a plurality of phrasifications based on the words as ordered combinations of leaf nodes.

3. The method of claim 1, wherein scoring each candidate phrasification comprises:

applying a factor to account for a confidence level of each component phrase in the candidate phrasification.

4. The method of claim 1, wherein scoring each candidate phrasification further comprises:

applying the probability of occurrence of each component phrase, and the number of component phrases in the candidate phrasification to a function that includes at least one parameter for adjusting precision and recall of the candidate phrasification.

5. The method of claim 1, wherein scoring each phrasification comprises:

applying a scoring model;

6. The method of claim 1, wherein the probability of occurrence of the component phrases of a candidate phrasification is based on an estimated probability figure.

7. The method of claim 1, wherein the probability of occurrence of the component phrases of a candidate phrasification is based on a proportion of documents to be searched that contain the component phrase.

8. The method of claim 1, wherein decomposing the input text into a candidate phrasification includes grouping and rearranging words of the input text.

9. The method of claim 1, wherein decomposing the input text includes replacing one or more of the input words with a related word.

10. The method of claim 1, wherein decomposing the input text includes replacing one or more of the component phrases with a related phrase.

11. The method of claim 10, further comprising assigning a confidence level to component phrases of a candidate phrasification, wherein component phrases not present in the input text are assigned a lesser confidence level than a component phrases present in the original input text.

12. The method of claim 10, wherein a related phrase includes a phrase synonymous with one of the component phrases.

13. The method of claim 10, wherein a related phrase includes a spell-corrected version of one of the component phrases.

14. The method of claim 10, wherein a related phrase includes a phrase chosen for semantic similarity to at least one of the component phrases.

15. The method of claim 1, wherein related words of the input text include synonyms of the words of the input text.

16. The method of claim 1, wherein related words of the input text include words chosen for semantic similarity to at least one of the words of the input text.

17. The method of claim 1, wherein the probability of occurrence of each component phrase in a candidate phrasification is based on an incidence of the component phrase in an indexed document collection.

18. The method of claim 1, wherein the probability of occurrence of each component phrase in a candidate phrasification is based on in natural language.

19. A system comprising:

a computer program product stored on a tangible computer readable medium for identifying valid phrases in an input text comprising a plurality of three or more words and comprising instructions that when executed cause a computer system to;

decompose the input text into a plurality of candidate phrasifications, including different groupings of words of the input text, each candidate phrasification comprising a disjoint union of component phrases, and each component phrase including at least one word or related word of the input text;

score at least two of the candidate phrasifications, wherein the candidate phrasifications include two or more component phrases, and wherein the scoring is based on a probability of occurrence of each of the candidate phrasification'"'"'s component phrases, and is based on the number of component phrases constituting the candidate phrasification, wherein candidate phrasifications having relatively fewer component phrases are weighted higher than candidate phrasifications having relatively more component phrases;

compare a score for each scored candidate phrasification to a threshold value;

select at least one candidate phrasification, wherein the scores of each selected phrasification exceeds a chosen threshold value; and

identify the phrases of each selected phrasification as valid phrases for the input text; and

one or more processors configured for executing the instructions.

20. The system of claim 19, wherein decomposing the input text comprises:

(i) restructuring a word tree derived from the input text, where the word tree comprises leaf nodes that are words and non-leaf nodes that are Boolean operators AND, OR, or NOT; and

(ii) generating a plurality of phrasifications based on the words as ordered combinations of leaf nodes.

21. The system of claim 19, wherein scoring each candidate phrasification comprises:

applying a factor to account for a confidence level of each component phrase in the candidate phrasification.

22. The system of claim 19, wherein scoring each candidate phrasification further comprises:

applying the probability of occurrence of each component phrase, and the number of component phrases in the candidate phrasification to a function that includes at least one parameter for adjusting precision and recall of the candidate phrasification.

23. The system of claim 19, wherein scoring each phrasification comprises:

applying a scoring model;

24. The system of claim 19, wherein the probability of occurrence of the component phrases of a candidate phrasification is based on an estimated probability figure.

25. The system of claim 19, wherein the probability of occurrence of the component phrases of a candidate phrasification is based on a proportion of documents to be searched that contain the component phrase.

26. The system of claim 19, wherein decomposing the input text into a candidate phrasification includes grouping and rearranging words of the input text.

27. The system of claim 19, wherein decomposing the input text includes replacing one or more of the input words with a related word.

28. The system of claim 19, wherein decomposing the input text includes replacing one or more of the component phrases with a related phrase.

29. The system of claim 28, wherein the computer program product further comprises instructions that when executed cause the computer system to:

assign a confidence level to component phrases of a candidate phrasification, wherein component phrases not present in the input text are assigned a lesser confidence level than a component phrases present in the original input text.

30. The system of claim 28, wherein a related phrase includes a phrase synonymous with one of the component phrases.

31. The system of claim 28, wherein a related phrase includes a phrase synonymous with one of the component phrases.

32. The system of claim 28, wherein a related phrase includes a spell-corrected version of one of the component phrases.

33. The system of claim 28, wherein a related phrase includes a phrase chosen for semantic similarity to at least one of the component phrases.

34. The system of claim 19, wherein the probability of occurrence of each component phrase in a candidate phrasification is based on an incidence of the component phrase in an indexed document collection.

35. The system of claim 19, wherein related words of the input text include synonyms of the words of the input text.

36. The system of claim 19, wherein related words of the input text include words chosen for semantic similarity to at least one of the words of the input text.

37. The system of claim 19, wherein the probability of occurrence of each component phrase in a candidate phrasification is based on an incidence of the component phrase in an indexed document collection.