Method for mining, mapping and managing organizational knowledge from text and conversation
First Claim
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1. A method for categorizing text, comprising the steps of:
- dividing the text into sentences;
parsing the sentences into one or more noun phrases;
converting the noun phrases into networks of word relationships by linking sequentially occurring noun phrases within each sentence; and
analyzing the networks of word relationships to determine the influence of each word by utilizing betweenness centrality, wherein each centering noun phrase in the networks of word relationships is centrally related with respect to peripheral words to the centering noun phrase while the peripheral words have no relationship between one another so that any association between the peripheral words must pass through the centering noun phrase.
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Abstract
A network text analysis called center resonance analysis (CRA) is presented which represents texts (or transcribed conversations) as networks of centering words. The networks of centering words are components of utterances or (specifically noun phrases) that authors and/or speakers deploy in a manner that makes their utterances coherent. A CRA network can be derived for any text, and abstractly represents its main concepts, their influence, and their interrelationships.
27 Citations
31 Claims
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1. A method for categorizing text, comprising the steps of:
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dividing the text into sentences; parsing the sentences into one or more noun phrases; converting the noun phrases into networks of word relationships by linking sequentially occurring noun phrases within each sentence; and analyzing the networks of word relationships to determine the influence of each word by utilizing betweenness centrality, wherein each centering noun phrase in the networks of word relationships is centrally related with respect to peripheral words to the centering noun phrase while the peripheral words have no relationship between one another so that any association between the peripheral words must pass through the centering noun phrase. - View Dependent Claims (2, 3, 4, 5)
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6. A method for categorizing text comprising the steps of:
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dividing the text into sentences; parsing the sentences into one or more noun phrases; converting words in the noun phrases into networks of word relationships; and analyzing the word relationship networks to determine the influence of each word by determining influence by utilizing the following formula; where I is the influence of a word (i) in the text (T) where gjk is the number of shortest paths connecting the jth and kth words, gjk (i) is the number of those paths containing word (i), and N is the number of words in the network.
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7. A method for analyzing text comprising the steps of:
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dividing the text into sentences; parsing the sentences into one or more noun phrases; converting one or more words within each of the noun phrases into networks of relationships between words; analyzing the networks to determine the influence for each word by utilizing betweenness centrality, wherein each centering noun phrase in the networks of word relationships is centrally related with respect to peripheral words to the centering noun phrase while the peripheral words have no relationship between one another so that any association between the peripheral words must pass through the centering noun phrase; and applying the analyzed networks to perform a specific analysis task. - View Dependent Claims (8, 9, 10, 11, 12, 13)
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14. A method for determining resonance based on common words in two sets of text comprising the step of utilizing the following formula:
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where WRAB is the word resonance between texts A and B, {w1A, w2A, . . . wN(A)A) are unique words for text A after parsing into phrases where N(A) is the number of unique words in text A, {I1A, I2A, . . . IN(A)A} are influence scores calculated for the unique words in text A, {w1B, w2B, . . . wN(B)B) are unique words for text B after parsing into phrases where N(B) is the number of unique words in text B, {I1B, I2B, . . . IN(B)B} are influence scores calculated for the unique words in text B, and indicator function α
ABij is equal to 1 if wiA and wjB are the same words, and the indicator function is equal to zero if wiA and wiB are not the same words.- View Dependent Claims (15, 16, 17, 18, 19)
where WRAB′
is the standardized word resonance between texts A and B, WRAB is the actual word resonance between texts A and B,is the sum of all influence scores for the unique words in text A squared, and is the sum of all influence scores for the unique words in text B squared.
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16. A method for searching two or more texts utilizing resonance scores obtained in accordance with claim 14.
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17. A method for searching two or more texts utilizing resonance scores obtained in accordance with claim 15.
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18. A method for modeling two or more texts utilizing resonance scores obtained in accordance with claim 14.
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19. A method for modeling two or more texts utilizing resonance scores obtained in accordance with claim 15.
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20. A method for determining pair resonance based on common word pairs in two sets of text comprising the step of utilizing the following formula:
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where PRAB is the word pair resonance between texts A and B, PijA is the frequency weighted pair influence of words i and j in text A and is equal to IiA·
IjA·
fijA where FijA is the number of times that wiA and wjA co-occur in text A, PijB is the frequency weighted pair influence of words k and 1 in text B and is equal to IkB·
IlB·
FklB where FklB is the number of times that wkB and wlB co-occur in text B, and indicator function β
ijklAB is equal to 1 if the two word sets (wiA, wjA) and (wkB, wlB) are equivalent and if FijA and FklB both are equal to one, otherwise the indicator is zero.- View Dependent Claims (21, 22, 23, 24, 25)
where PR′
AB is the standardized word pair resonance between texts A and B and PRAB is the actual word pair resonance between texts A and B.
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22. A method for searching two or more texts utilizing resonance scores obtained in accordance with claim 20.
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23. A method for searching two or more texts utilizing resonance scores obtained in accordance with claim 21.
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24. A method for modeling two or more texts utilizing resonance scores obtained in accordance with claim 20.
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25. A method for modeling two or more texts utilizing resonance scores obtained in accordance with claim 21.
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26. A method for analyzing text comprising the steps of:
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a) compartmentalizing the text into defined units; b) categorizing the defined units by; parsing the units into one or more noun phrases each comprising one or more words, converting the word or words into networks of relationships between words by linking sequentially occurring noun phrases within a defined unit, and analyzing the networks of word associations to determine the structural influence of each word by utilizing betweenness centrality, wherein each centering noun phrase in the networks of word relationships is centrally related with respect to peripheral words to the centering noun phrase while the peripheral words have no relationship between one another so that any association between the peripheral words must pass through the centering noun phrase; and c) applying the analyzed network to perform a specific analysis task. - View Dependent Claims (27, 28, 29, 30, 31)
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Specification