Source: https://patents.google.com/patent/US8631027
Timestamp: 2018-04-19 09:57:08
Document Index: 619029161

Matched Legal Cases: ['Application No. 08799272', 'Application No. 2010200478', 'Application No. 08799272', 'Application No. 08799272', 'Application No. 2', 'Application No. 2', 'Application No. 10', 'Application No. 10', 'Application No. 10', 'Application No. 2008296071', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 200510085371', 'Application No. 200510085371', 'Application No. 200510085371', 'Application No. 200680007173', 'Application No. 200880105846', 'Application No. 201110198660', 'Application No. 201110198660', 'Application No. 201110200364', 'Application No. 201110200364', 'Application No. 201110200374', 'Application No. 201110200374', 'Application No. 05254647', 'Application No. 06719537', 'Application No. 05254646', 'Application No. 06719537', 'Application No. 10', 'Application No. 10', 'Application No. 10', 'Application No. 2005', 'Application No. 20053637', 'Application No. 200510085371', 'Application No. 10', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 2', 'Application No. 200510085371', 'Application No. 20080105846', 'Application No. 201110198660', 'Application No. 201110200364', 'Application No. 201110200374', 'Application No. 05254644', 'Application No. 05254646', 'Application No. 05254647', 'Application No. 06719537', 'Application No. 2005216529', 'Application No. 10', 'Application No. 10', 'Application No. 10', 'Application No. 2005']

US8631027B2 - Integrated external related phrase information into a phrase-based indexing information retrieval system - Google Patents
Integrated external related phrase information into a phrase-based indexing information retrieval system Download PDF
US8631027B2
US8631027B2 US13347467 US201213347467A US8631027B2 US 8631027 B2 US8631027 B2 US 8631027B2 US 13347467 US13347467 US 13347467 US 201213347467 A US201213347467 A US 201213347467A US 8631027 B2 US8631027 B2 US 8631027B2
US13347467
US20120197885A1 (en )
This application is a continuation of U.S. application Ser. No. 11/851,962, filed on Sep. 7, 2007, which is related to the following applications: Phrase Identification in an Information Retrieval System, application Ser. No. 10/900,021, filed on Jul. 26, 2004, now issued as U.S. Pat. No. 7,580,921 on Aug. 25, 2009; Phrase-Based Indexing in an Information Retrieval System, application Ser. No. 10/900,055, filed on Jul. 26, 2004, now issued as U.S. Pat. No. 7,536,408 on May 19, 2009; Phrase-Based Searching in an Information Retrieval System, application Ser. No. 10/900,041, filed on Jul. 26, 2004, now issued as U.S. Pat. No. 7,599,914 on Oct. 6, 2009; Phrase-Based Personalization of Searches in an Information Retrieval System, application Ser. No. 10/900,039, filed on Jul. 26, 2004, now issued as U.S. Pat. No. 7,580,929 on Aug. 25, 2009; Automatic Taxonomy Generation in Search Results Using Phrases, application Ser. No. 10/900,259, filed on Jul. 26, 2004, now issued as U.S. Pat. No. 7,426,507 on Sep. 16, 2008; and Phrase-Based Detection of Duplicate Documents in an Information Retrieval System, application Ser. No. 10/900,012, filed on Jul. 26, 2004, now issued as U.S. Pat. No. 7,711,679 on May 4, 2010; all of which are co-owned, and incorporated by reference herein.
M(p)>5 (the number of interesting instances of phrase p is more than 5).
i) compute the expected value E(gk). The expected co-occurrence rate E(j,k) of gj and gk, if they were unrelated phrases is then E(g)*E(gk);
Accordingly, each phrase gj remaining on the good phrase list 208 will predict some number of other phrases, based on the information gain threshold previously discussed. Now, for each phrase gj the indexing system 110 performs a string match with each of the phrases gk that is predicts. The string match tests whether each predicted phrase gk is a phrase extension of the phrase gj. If all of the predicted phrases gk are phrase extensions of phrase then phrase gj is incomplete, and is removed from the good phrase list 208, and added to an incomplete phrase list 216. Thus, if there is at least one phrase gk that is not an extension of gj, then gj is complete, and maintained in the good phrase list 208. For example then, “President of the United” is an incomplete phrase because the only other phrase that it predicts is “President of the United States” which is an extension of the phrase.
Accordingly, any entry (gj, gk) that is less the Related Phrase threshold is zeroed out, indicating that the phrases gk are not related. Any remaining entries in the co-occurrence matrix 212 now indicate all related phrases.
For each related phrase m in Rj, the indexing system 110 determines if each of the other related phrases in R is also related to gi. Thus, if I(gk, gl) is also non-zero, then gj, gk, and g l are part of a cluster. This cluster test is repeated for each pair (gl, gm) in R.
To summarize then, after this process there will be identified for each good phrase a set of related phrases R, which are sorted in order of information gain I(gj, gk) from highest to lowest. In addition, for each good phrase there will be a cluster bit vector, the value of which is a cluster number identifying the primary cluster of which the phrase gj is a member, and the orthogonality values (1 or 0 for each bit position) indicating which of the related phrases in R are in common clusters with Thus in the above example, “Bill Clinton”, “President”, and “Monica Lewinsky” are in cluster 14 based on the values of the bits in the row for phrase “Bill Clinton”.
For each good phrase g1 (example g1 “President” and g4 “President of ATT”) post the document identifier (e.g., the URL) to the posting list for the good phrase gi in the index 150. This update identifies that the good phrase gi appears in this specific document.
In one embodiment, the related phrase information is a related phase bit vector. This bit vector may be characterized as a “bi-bit” vector, in that for each related phrase gk there are two bit positions, gk−1, gk−2. The first bit position stores a flag indicating whether the related phrase gk is present in the document d (i.e., the count for gk in document d is greater than 0). The second bit position stores a flag that indicates whether a related phrase gj of gk is also present in document d. The related phrases gl of a related phrase gk of a phrase gj are herein called the “secondary related phrases of gj” The counts and bit positions correspond to the canonical order of the phrases in R (sorted in order of decreasing information gain). This sort order has the effect of making the related phrase gk that is most highly predicted by gj associated with the most significant bit of the related phrase bit vector, and the related phrase gl that is least predicted by gj associated with the least significant bit.
iii) for each related phrase gk present in document d, which of its related phrases gi (the secondary related phrases of gj) are also present in document d.
Next, the inlink score is determined as follows. For each inlink to URL1 containing the anchor phrase A, the indexing system 110 scans URL1, and determines whether phrase A appears in the body of URL1. If phrase A not only points to URL1 (via a outlink on URL0), but also appears in the content of URL1 itself, this suggests that URL1 can be said to be intentionally related to the concept represented by phrase A. FIG. 7 b illustrates this case, where phrase A appears in both URL0 (as anchor text) and in the body of URL1. In this case, the related phrase bit vector for phrase A for URL1 is used as the inlink score for the link from URL0 to URL1 containing phrase A.
As described above, for any given phrase each document d in the gj's posting list has an associated related phrase bit vector that identifies which related phrases gk and which secondary related phrases gl are present in document d. The more related phrases and secondary related phrases present in a given document, the more bits that will be set in the document's related phrase bit vector for the given phrase. The more bits that are set, the greater the numerical value of the related phrase bit vector.
1. A computer-implemented method for updating top phrases associated with a limited document collection, the method comprising:
storing top phrases for the limited document collection, wherein the top phrases are related to related phrases and wherein both the top phrases and related phrases of the top phrases are present in the document collection;
receiving, by at least one processor of a computer system, from a user a replacement top phrase for a top phrase;
defining, by at least one processor of the computer system, the replacement top phrase as a new top phrase for the document collection; and
associating, by at least one processor of the computer system, the replacement top phrase with a root document of the document collection.
2. The method of claim 1, wherein the limited document collection includes at least some webpages of a website.
3. The method of claim 2, wherein the root document is a root document of the website.
prompting a user to provide authentication credentials;
receiving authentication credentials from the user;
identifying the received authentication credentials as indicating authority of the user over the limited document collection;
based on identifying the received authentication credentials as indicating authority of the user over the limited document collection, displaying at least some of the top phrases associated with the limited document collection to the user.
based on identifying the received authentication credentials as indicating authority of the user over the limited document collection, allowing the user to submit the received replacement phrase.
6. The method of claim 1, wherein related phrases of a top phrase are identified in a posting list for the top phrase.
7. The method of claim 1, further comprising determining the top phrases of the limited document collection, wherein the determining includes:
for each of a plurality of phrases contained in documents of the limited document collection:
identifying the phrase in different documents of the document collection;
determining per-document importance scores for the identified phrase based on occurrences of related phrases of the identified phrase in each of the different documents;
for each of the plurality of phrases, determining an aggregate score of the phrase for the limited document collection based on the phrase's per-document importance scores; and
selecting phrases as the top phrases based on the aggregate scores of the phrases.
8. The method of claim 7, wherein related phrases of an identified phrase are identified in a posting list for the identified phrase.
9. The method of claim 7, wherein a per-document importance score of an identified phrase is based on a frequency of the related phrases of the identified phrase in the document.
10. The method of claim 7, wherein determining an aggregate score for a phrase comprises summing the per-document importance scores for the phrase.
identifying the replacement phrase and the top phrase for which the replacement phrase is received as related phrases of each other.
adding to phrase information for the replacement phrase, related phrase information for the top phrase for which the replacement phrase is received.
13. The method of claim 1, wherein associating the replacement top phrase with the root document of the document collection includes adding the root document for the document collection to a posting list for the replacement top phrase.
14. A system for updating top phrases associated with a limited document collection, the system comprising:
a tangible, non-transitory computer readable medium storing instructions that when executed by the one or more processors cause the system to:
store top phrases for the limited document collection, wherein the top phrases are related to related phrases and wherein both the top phrases and related phrases of the top phrases are present in the document collection;
receive, by at least one processor of a computer system, from a user a replacement top phrase for a top phrase;
define, by at least one processor of the computer system, the replacement top phrase as a new top phrase for the document collection; and
associate, by at least one processor of the computer system, the replacement top phrase with a root document of the document collection.
15. The system of claim 14, wherein the limited document collection includes at least some webpages of a website.
16. The system of claim 15, wherein the root document is a root document of the website.
19. The system of claim 14, wherein related phrases of a top phrase are identified in a posting list for the top phrase.
20. The system of claim 14, further comprising determining the top phrases of the limited document collection, wherein the determining includes:
21. The system of claim 20, wherein related phrases of an identified phrase are identified in a posting list for the identified phrase.
22. The system of claim 20, wherein a per-document importance score of an identified phrase is based on a frequency of the related phrases of the identified phrase in the document.
23. The system of claim 20, wherein determining an aggregate score for a phrase comprises summing the per-document importance scores for the phrase.
26. The system of claim 14, wherein associating the replacement top phrase with the root document of the document collection includes adding the root document for the document collection to a posting list for the replacement top phrase.
US13347467 2007-09-07 2012-01-10 Integrated external related phrase information into a phrase-based indexing information retrieval system Active US8631027B2 (en)
US11851962 Continuation US8117223B2 (en) 2007-09-07 2007-09-07 Integrating external related phrase information into a phrase-based indexing information retrieval system
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