Building and updating of co-occurrence dictionary and analyzing of co-occurrence and meaning

• US 5,406,480 A
• Filed: 01/15/1993
• Issued: 04/11/1995
• Est. Priority Date: 01/17/1992
• Status: Expired due to Term

• Current Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Original Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Inventors: Kanno, Yuji
• Primary Examiner(s): Hayes, Gail O.
• Assistant Examiner(s): CHUNG TRANS, XUONG MY
• Application Number:
  US08/004,029
• Time in Patent Office: 816 Days
• Field of Search: 364/419.02, 364/419.08, 364/419.11
• US Class Current: 704/10
• CPC Class Codes: G06F 40/247 Thesauruses; Synonyms
• CPC Class Codes: G06F 40/253 Grammatical analysis; Style critique
• CPC Class Codes: G06F 40/284 Lexical analysis, e.g. tokenisation or collocates

Abstract

A co-occurrence dictionary is built through a process for calculating three kinds of co-occurrence information and a real number vector corresponding to each category. The co-occurrence dictionary is updated through a process for selecting the opposite phrase of the co-occurrence for the additional co-occurrence information and a process for calculating a real number vector corresponding to an additional word on the basis of the additional co-occurrence information. A co-occurrence analysis is effected through a process for calculating in real number the degree of the co-occurrence on the basis of the real number vectors corresponding to two categories to be checked in the co-occurrence relation, and a semantic analysis is effected through a process for indicating, by a numerical value, the propriety of the interpretation on the basis of the degree of each co-occurrence.

Claims

1. A computer implemented method, implemented by a programmed computer, of building a co-occurrence dictionary describing whether phrases co-occur in one sentence, the phases belonging to first and second categories in a dictionary containing phrases of a natural language which is an object, said method comprising using the computer to build the co-occurrence dictionary by implementing the steps of:

selecting, as a first sub-group of phrases (11), phrases from a first group of phrases (1) comprising all phrases belonging to said first category in said dictionary;

selecting, as a second sub-group of phrases (21), phrases from a second group of phrases (2) comprising all phrases belonging to said second category in the dictionary;

preparing first co-occurrence information describing whether each phrase belonging to the first sub-group (11) and each phrase belonging to the second sub-group (21) co-occur in one sentence of the object language;

preparing second-co-occurrence information describing whether each phrase belonging to a third sub-group of phrases (12), comprising all the phrases in the first group (1) which do not belong to the first sub-group (11) and each phrase belonging to the second sub-group (21), co-occur in one sentence of the object language;

preparing third co-occurrence information describing whether each phrase belonging to a fourth sub-group of phrases (22), comprising all the phrases in the second group (2) which do not belong to the second sub-group (21) and each phrase belonging to the first sub-group (11) co-occur in one sentence of the object language;

arranging the first co-occurrence information such that each phrase belonging to the first sub-group (11) corresponds to a real number vector with a dimension below a common maximum dimension and each phrase belonging to the second sub-group (21) corresponds to a real number vector with a dimension below the common maximum dimension;

calculating a value of the real number vector corresponding to each phrase in the first sub-group (11) and a value of the real number vector corresponding to each phrase in the second sub-group (21) on the basis of the first co-occurrence information so that the number of sets of two phrases, wherein;

a value of an inner product of the real number vector corresponding to a first phrase and the real number vector corresponding to a second phrase becomes positive when describing, in the first co-occurrence information, that a first phrase belonging to said first sub-group (11) and a second phrase belonging to said second sub-group (21) co-occur in one sentence, and

the value of an inner product of the real number vector corresponding to said first phrase and the real number vector corresponding to said second phrase becomes negative when describing, in said first co-occurrence information, that said first phrase belonging to said first sub-group (11) and said second phrase belonging to said second sub-group (21) do not co-occur in one sentence,

becomes the greatest of all the numbers of sets each comprising phrases belonging to said first sub-group (11) and phrases belonging to the second sub-group (21);

arranging said second co-occurrence information such that each phrase belonging to said third sub-group (12) corresponds to a real number vector with a dimension below the maximum dimension;

calculating a value of the real number vector corresponding to each phrase in said third sub-group (12) on the basis of said second co-occurrence information so that the number of sets of two phrases, wherein;

a value of the inner product of the real number vector corresponding to a third phrase belonging to said third sub-group (12) and the real number vector corresponding to a fourth phrase belonging to said second sub-group (21) and calculated on the basis of said first co-occurrence information becomes positive when describing, in said second co-occurrence information, that the third phase and the fourth phrase co-occur in one sentence, and

a value of an inner product of the real number vector corresponding to the third phrase and the real number vector corresponding to the fourth phrase becomes negative when describing, in said second co-occurrence information, that the third phrase and the fourth phrase do not co-occur in one sentence,

becomes the largest of all the numbers of sets each comprising a phrase belonging to said third sub-group (12) and a phrase belonging to said second sub-group (21);

arranging said third co-occurrence information such that each phrase belonging to the fourth sub-group (22) corresponds to a real number vector with a dimension below the maximum dimension; and

calculating a value of the real number vector corresponding to each phrase in the fourth sub-group (22) on the basis of said third co-occurrence information so that the number of sets of two phrases, wherein;

the inner product of the real number vector corresponding to a fifth phrase belonging to said first sub-group (11) and calculated on the basis of said first co-occurrence information and the real number vector corresponding to a sixth phrase belonging to the fourth sub-group (22) becomes positive when describing, in the third co-occurrence information, that the fifth phrase and the sixth phrase co-occur in one sentence and, on the other hand,

the inner product of the real number vector corresponding to the fifth phrase calculated on the basis of the first co-occurrence information and the real number vector corresponding to the sixth phrase becomes negative when describing, in the third co-occurrence information, that the fifth phrase and the sixth phrase do not co-occur in one sentence,

becomes the greatest of all the numbers of sets each comprising a phrase belonging to said first sub-group (11) and a phrase belonging to said fourth sub-group (22).

2. A method as claimed in claim 1, comprising the further step of:

correcting said first co-occurrence information by exceptionally reversing the decision of the co-occurrence with respect to a portion of said first co-occurrence information so that the number of sets of two phrases, wherein;

the value of the inner product of the real number vector corresponding to said first phrase belonging to said first sub-group (11) and the real number vector corresponding to said second phrase belonging to said second sub-group (21) becomes positive when describing that said first phrase and said second phrase co-occur in one sentence and the value of the inner product of the real number vector corresponding to said first phrase and the real number vector corresponding to said second phrase becomes negative when describing, in the first co-occurrence information, that the first phrase and the second phrase do not co-occur in one sentence,

is above a constant rate to the number of all sets each comprising phrases belonging to the first sub-group (11) and phrases belonging to said second sub-group (21), and the corrected first co-occurrence information is used as said first co-occurrence information and the co-occurrence information are calculated in real number vector form with respect to all the phrases of said first group of phrases (1) and said second group of phrases (2) so as to calculate the co-occurrence information in the real number vector form and in exception information form.

3. A method as claimed in claim 1, further comprising the steps of:

when a new seventh phrase belonging to a first category is added to the built co-occurrence dictionary which describes whether each phrase belonging to said first category and each phrase belonging to a second category co-occur in one sentence in a dictionary containing phrases of a natural language,

selecting a first select group of phrases in said dictionary, which consists of N phrases, of phrases belonging to said second category and which are above a maximum dimension of the corresponding vectors and in which the absolute value of an inner product of the real number vectors corresponding to every two phrases is below a constant value, so as to give additional co-occurrence information indicative of whether the N phrases and the seventh phrase co-occur in one sentence of said language;

arranging that said seventh phrase corresponding to a real number vector having a dimension below the maximum dimension; and

calculating a real number vector corresponding to said seventh phrase so that the number M of sets of two phrases, wherein

a value of the inner product of the real number vector corresponding to said seventh phrase and the real number vector corresponding to an eighth phrase belonging to the first select group of phrases becomes positive when describing, in said additional co-occurrence information, that said eighth phrase and said seventh phrase co-occur in one sentence and the value of the inner product of the real number vector corresponding to said seventh phrase and the real number vector corresponding to said eighth phrase becomes negative when describing, in said additional co-occurrence information, that the eighth phrase and the seventh phrase do not co-occur in one sentence,

has a maximum so that the calculated real number vector is added as the co-occurrence information for the seventh phrase to said co-occurrence dictionary.

4. A method as claimed in claim 3, comprising the further step of, when the number M is below a predetermined number L,

selecting a second select group of phrases whose number is constant from the second category to give readditional co-occurrence information indicative of whether said second select group of phrases and said seventh phrase co-occur in one sentence of the language so as to correct said additional co-occurrence information so that the co-occurrence decisions of said additional co-occurrence information and a portion of said readditional co-occurrence information are exceptionally reversed, and calculating the real number vector corresponding to said seventh phrase on the basis of the corrected additional co-occurrence information so that the number M become above the predetermined number L, and adding the calculated real number vector as the co-occurrence information for said seventh phrase to said co-occurrence dictionary.

5. A method as claimed in claim 3, further comprising the step of performing a co-occurrence analysis using said co-occurrence dictionary to automatically decide whether phrases which are included in said dictionary and which belong to first and second categories co-occur in one sentence, wherein, when said first phrase included in said first category in said co-occurrence dictionary and said second phrase included in said second category in said co-occurrence dictionary appear at positions, allowable on morphpheme and syntax, in the sentence to be analyzed, if the inner product of the real number vector corresponding to the first phrase and the real number vector corresponding to the second phrase is positive, a decision is made that said first phrase and said second phrase co-occur, and on the other hand, if the inner product of the real number vector corresponding to said first phrase and the real number vector corresponding to said second phrase is negative, a decision is made that said first phrase and said second phrase do not co-occur.

6. A method as claimed in claim 5, wherein, when said first phrase and said second phrase in the sentence to be analyzed are vague on a morphpheme and syntax, using the interpretation that the absolute value of the inner product of the real number vector corresponding to said second phrase and the real number vector corresponding to said first phrase calculated in accordance with the co-occurrence analysis method is the greatest value or a group of interpretations that the absolute value of the inner product is above a constant value, and rejecting the other interpretations.

7. A method as claimed in claim 6, wherein the natural language is the Japanese language, said first category is nouns, and said second category is deep cases of a predicate.

8. A method as claimed in claim 7, further comprising the steps of:

calculating an inner product Q of a real number vector corresponding to each deep case in a plurality of deep case patterns P of the predicate in a sentence S to be analyzed and a real number vector corresponding to a noun which is applied to each deep case in the sentence S; and

adding a weight proper to each deep case in the deep case patterns P to the inner product Q to adopt the deep case pattern, that the added value E is the greatest, as an interpretation for the deep case pattern of the predicate in the sentence S.

9. A method as claimed in claim 7, further comprising the steps of:

calculating an inner product Q of a real number vector corresponding to each deep case in a plurality of deep case patterns P of the predicate in a sentence S to be analyzed and a real number vector corresponding to a noun which is applied to each deep case in the sentence S; and

adding a weight proper to each deep case in the deep case patterns P to the inner product Q to adopt all the deep case patterns, that the added value E is above a predetermined constant value, as an interpretation for the deep case pattern of the predicate in the sentence S.