Information retrieval through indentification of prominent notions

• US 20110093257A1
• Filed: 10/19/2009
• Published: 04/21/2011
• Est. Priority Date: 10/19/2009
• Status: Active Grant

• Current Assignee: Avraham Shpigel, Shalom Wintner
• Original Assignee: Avraham Shpigel, Shalom Wintner
• Inventors: Wintner, Shalom, Shpigel, Avraham
• Granted Patent: US 8,375,033 B2
• Time in Patent Office: Days
• US Class Current: 704/9
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars

Abstract

A system and method for information retrieval from a corpus of text based on offline prominent sentences extraction, and online prominent sentences retrieval ordered by predefined criteria, and recommending online cross-interest prominent sentences.

Claims

1. A system and method for information retrieval comprising the steps of:

a. extracting offline prominent sentences from a corpus of text;

b. retrieving online prominent sentences ordered by criteria;

c. recommending online cross-interest prominent sentences.

2. The method in claim 1, wherein said extracting offline prominent sentences further comprising the step of scoring prominent sentences according to at least one of the following parameters:

sentence structure, extracted rule, frequency of NP subject, frequency of NP subject head, and combination thereof.

3. The method in claim 2, wherein said sentence structure comprising from left to right:

NP subject, verb pattern, text following said verb pattern, dot.

4. The method in claim 2, wherein said extracted rule further comprising the step of:

a. extracting said sentence structure;

b. extracting verb pattern from sentence;

c. scoring the sentence and its verb pattern;

d. summing the score of the verb pattern over all the occurrences of the pattern in the corpus;

e. Verb pattern is referred to as rule if its score is greater than a pre-defined threshold.

5. The method in claim 4, wherein said scoring sentence further includes the summing of the scores of at least one of the following parameters:

number of occurrences of the NP subject in the same document both before and after the occurrence of the pattern, number of occurrences of the NP subject head of the subject in the same document both before and after the occurrence of the pattern, length in words of the NP subject, length in words of the verb cluster, length in words of the sentence, sentence tense, sentence aspect, sentence mood, sentence voice of the verbs in the pattern, subject is available in an existing library of concepts, NP subject head is available in an existing library of concepts, and combined thereof.

6. The method in claim 4, wherein said verb pattern comprising from at least one of:

verb cluster, the determination word of the object followed the verb cluster, punctuation, and combined thereof.

7. The method in claim 1, wherein said retrieving online prominent sentences ordered by criteria further comprising from at least one of:

Search keywords cited in document, Document type, Number of search keywords cited in a single prominent sentence, Search keywords cited in the Subject part of the prominent sentence, Formal definition, Number of words in Subject, Number of words in prominent sentence, The score of the prominent sentence, Document creation date, Frequency of interest in prominent sentence, Morphological information, Part of speech of a search keyword in prominent sentence, The ratio of all prominent sentences in document to the document length, Document text quality, Document size (in words) and combined thereof.

8. The method in claim 1, wherein said recommending cross-interest prominent sentences further comprising the step of:

a. user interested in at least two prominent sentences on the same session;

b. updating the statistic of cross-interested prominent sentences;

c. recommend at least one cross-interest prominent sentence to at least one user.

9. The method in claim 1, wherein said corpus of text further comprising from at least one of:

any single document, any collection of documents, organizational project documents, organizational documents, any vertical corpus of text, corpora, and combined thereof.

10. A computer software product for searching and retrieving prominent sentences, comprising a computer-readable medium in which program instructions are stored, which instructions, when read by a computer, cause the computer to:

a. extracting offline prominent sentences from a corpus of text;

b. retrieving online prominent sentences ordered by criteria;

c. recommending online cross-interest prominent sentences.