CA1306062C - Computer information retrieval using latent semantic structure - Google Patents
Computer information retrieval using latent semantic structureInfo
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- CA1306062C CA1306062C CA000596524A CA596524A CA1306062C CA 1306062 C CA1306062 C CA 1306062C CA 000596524 A CA000596524 A CA 000596524A CA 596524 A CA596524 A CA 596524A CA 1306062 C CA1306062 C CA 1306062C
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Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/10—Text processing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10—TECHNICAL SUBJECTS COVERED BY FORMER USPC
- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
- Y10S707/99935—Query augmenting and refining, e.g. inexact access
Abstract
Abstract of the Disclosure A methodology for retrieving textual data objects is disclosed. The information is treated in the statistical domain by presuming that there is an underlying, latent semantic structure in the usage of words in the data objects. Estimates to this latent structure are utilized to represent and retrieve objects. A user query is recouched in the new statistical domain and then processed in the computer system to extract the underlying meaning to respond to the query.
Description
~3~
This invention relates generally to computer-based i~formation retrieval and, in particular, to user accessibility to and display of textual material stored in computer files.
S E~çkg~ of the Invention Increases in computer storage capacity, transmission rates and processing speed mean that many large and important collections of data are now available electronically, such as via bulletin boards, mail, and on-line texts, documents and directories. While many of the technological barriers to information access and display have I0 been removed, the human/system interface problem of being able to locate what one really needs from the collections remains. Methods for storing, organizing and accessing this information range from electronic analogs of familiar paper-based techniques, such as tables of contents or indices to richer associative connections that are feasible only wi1h computers, such as hypertext and full-context addressability. While these techniques may provide 15 retrieval benefits over the prior paper-based techniques, many advantages of electronic storage are yet unrealized. Most systems still require a user or provider of information to specify explicit relationships and links b~ tween data objects or text objects, thereby making the systems tedious to use or to apply to large, heterogeneous computer information files whose content may be unfamiliar to the user.
To exemplify one standard approach whose difficulties and deficiencies are representative of conventioDal approaches, the retrieval of information using keyword matching is considered. This technique depends Dn matching individual words in a user's request vith individual words in the total database of textual material. Text objects that contain one or more words in common with those in the user's query are return~d as 25 relevant. Keyword-based retrieval systems like this are, however, far from ideal. Many objects relevant to the query may be missed, and oftentimes unrelated objects are retrieved.
The fundamental deficiency of current information retrieval methods is that the words a searcher uses are often not the same as those by which the information sought has been indexed. There are actually t vo aspects to the problem. First, there is a 30 tremendous diversity in the words people use to describe the same object or concept; this is called synonymy. Users in different contexts, or with different needs, knowledge or linguistic habits will describe the same information using different terms. For example, it has been demonstrated that any two people choose the same main keyword for a single, well-known object less than 20% of the time on average. Indeed, this variability is much ~3~
This invention relates generally to computer-based i~formation retrieval and, in particular, to user accessibility to and display of textual material stored in computer files.
S E~çkg~ of the Invention Increases in computer storage capacity, transmission rates and processing speed mean that many large and important collections of data are now available electronically, such as via bulletin boards, mail, and on-line texts, documents and directories. While many of the technological barriers to information access and display have I0 been removed, the human/system interface problem of being able to locate what one really needs from the collections remains. Methods for storing, organizing and accessing this information range from electronic analogs of familiar paper-based techniques, such as tables of contents or indices to richer associative connections that are feasible only wi1h computers, such as hypertext and full-context addressability. While these techniques may provide 15 retrieval benefits over the prior paper-based techniques, many advantages of electronic storage are yet unrealized. Most systems still require a user or provider of information to specify explicit relationships and links b~ tween data objects or text objects, thereby making the systems tedious to use or to apply to large, heterogeneous computer information files whose content may be unfamiliar to the user.
To exemplify one standard approach whose difficulties and deficiencies are representative of conventioDal approaches, the retrieval of information using keyword matching is considered. This technique depends Dn matching individual words in a user's request vith individual words in the total database of textual material. Text objects that contain one or more words in common with those in the user's query are return~d as 25 relevant. Keyword-based retrieval systems like this are, however, far from ideal. Many objects relevant to the query may be missed, and oftentimes unrelated objects are retrieved.
The fundamental deficiency of current information retrieval methods is that the words a searcher uses are often not the same as those by which the information sought has been indexed. There are actually t vo aspects to the problem. First, there is a 30 tremendous diversity in the words people use to describe the same object or concept; this is called synonymy. Users in different contexts, or with different needs, knowledge or linguistic habits will describe the same information using different terms. For example, it has been demonstrated that any two people choose the same main keyword for a single, well-known object less than 20% of the time on average. Indeed, this variability is much ~3~
greater than commonly believed and this places strict, low limits on the expected performance of word-matching systems.
The second aspect relates to polysemy, a word havin~ more than one distinct meaning. In different contexts or when used by different people the same word S takes on varying referential significance (e.g., "bank" in river bank versus "bank" in a saviDgs bank). Thus the use of a term in a search query does not necessarily mean that a text object containing or labeled by the same term is of interest.
Because human word use is characterized by extensive synonymy and polysemy, straightforward term-matching schemes have serious shortcomings -- relevant 10 materials will be missed because different people describe the same topic using different words and, because the same word can have different meanings, irrelevant material will be retrieved. The basic problem may be simply summarized by stating that people want to access information based on meaning, but the words they select do not adequately e~press intended meaning. Previous attempts to improve standard word searching and overcome the 15 diversity in human word usage have involved: restricting the allowable vocabulary and training intermediaries to generate indexing and search keys; hand-crafting thesauri to provide synonyms; or coDstructing explicit models of the relPvant domain knowledge. Not only are these methods expert-labor intensive, but they are often not very successful.
SumrrlaIy Q~h~;~nvention These shortcomings as well as other deficiencies and limitations of information retrieval are obviated, in accordance with the present invention, byautomatically constructing a semaDtic space for rstrieval. This is effected by treating the unreliability of observed word-to-text object association data as a statistical problem. The basic postulate is that there is an Imderlying latent semantic structure in word usage data 25 that is partially hiddeD or obscured by the variability of word choice. A statistical approach is utilized to estimate this latent structure and uncover the latent meaning. Words, the te~t objects and, later, user queries are processed to extract this underlying meaning and the new, latent semantic structure domain is then used to represent and retrieve information.
The organization and operation of this invention will be better 30 understood from a consideration of the detailed description of the illustrative embodiment thereof, which follows, when taken iD conjunction with the accompanying drawing.
6~
The second aspect relates to polysemy, a word havin~ more than one distinct meaning. In different contexts or when used by different people the same word S takes on varying referential significance (e.g., "bank" in river bank versus "bank" in a saviDgs bank). Thus the use of a term in a search query does not necessarily mean that a text object containing or labeled by the same term is of interest.
Because human word use is characterized by extensive synonymy and polysemy, straightforward term-matching schemes have serious shortcomings -- relevant 10 materials will be missed because different people describe the same topic using different words and, because the same word can have different meanings, irrelevant material will be retrieved. The basic problem may be simply summarized by stating that people want to access information based on meaning, but the words they select do not adequately e~press intended meaning. Previous attempts to improve standard word searching and overcome the 15 diversity in human word usage have involved: restricting the allowable vocabulary and training intermediaries to generate indexing and search keys; hand-crafting thesauri to provide synonyms; or coDstructing explicit models of the relPvant domain knowledge. Not only are these methods expert-labor intensive, but they are often not very successful.
SumrrlaIy Q~h~;~nvention These shortcomings as well as other deficiencies and limitations of information retrieval are obviated, in accordance with the present invention, byautomatically constructing a semaDtic space for rstrieval. This is effected by treating the unreliability of observed word-to-text object association data as a statistical problem. The basic postulate is that there is an Imderlying latent semantic structure in word usage data 25 that is partially hiddeD or obscured by the variability of word choice. A statistical approach is utilized to estimate this latent structure and uncover the latent meaning. Words, the te~t objects and, later, user queries are processed to extract this underlying meaning and the new, latent semantic structure domain is then used to represent and retrieve information.
The organization and operation of this invention will be better 30 understood from a consideration of the detailed description of the illustrative embodiment thereof, which follows, when taken iD conjunction with the accompanying drawing.
6~
Br;ef nescription of the Drawin~
FIG. 1 is a plot of the "term" coordinates and the "document"
coordinates based s~n a two-dimensional singular value decomposition of an original "term-by-document" matrix; and FIG. 2 is a flow diagram depicting the processiDg to generate the "term" and "document" matrices using singular valui: decomposition as well as the processing of a user's query.
Detailed Descripti~
Before discussing the principles and operational characteristics of this 10 invention in detail, it i9 helpful to present a motivating e~ample. This also aids in introducing terminology utilized later in the discussion.
Simple Example Illustrafing the Method The coDtents of Table 1 are used to illustrate how semantic structure analysis works and to point out the diffe}ences between this method and conventional 15 keyword matching.
DOCUMENT S~3T BASED ON TITLES
c1: Human machine interface fo} Lab ABC computer applications c2: A survey of user opinion of computer system response time ~ -c3: The EPS user mterface management system c4: Systems and human systems engineering testing of EPS-2 cS: Relation of user-perceived response time to error measurement ml: The generation of random, binary, unordered trees m2: The intersection graph of paths in trees m3: Graph minors IV: Widths of trees and well-quasi-ordering m4: Graph minors: A survey In this example, a file of text objects consists of nine titles of technical documents with titles cl-cS concerned with human/computer interaction and titles ml-m4 concerned with mathematical graph theory. In Table 1, words occurring in more than one 30 title are italicized. Using conventional keyword retrieval, if a user requested papers dealing with "human compu~er interaction," titles c1, c2, and c4 would be returned, since these titles 6;~
FIG. 1 is a plot of the "term" coordinates and the "document"
coordinates based s~n a two-dimensional singular value decomposition of an original "term-by-document" matrix; and FIG. 2 is a flow diagram depicting the processiDg to generate the "term" and "document" matrices using singular valui: decomposition as well as the processing of a user's query.
Detailed Descripti~
Before discussing the principles and operational characteristics of this 10 invention in detail, it i9 helpful to present a motivating e~ample. This also aids in introducing terminology utilized later in the discussion.
Simple Example Illustrafing the Method The coDtents of Table 1 are used to illustrate how semantic structure analysis works and to point out the diffe}ences between this method and conventional 15 keyword matching.
DOCUMENT S~3T BASED ON TITLES
c1: Human machine interface fo} Lab ABC computer applications c2: A survey of user opinion of computer system response time ~ -c3: The EPS user mterface management system c4: Systems and human systems engineering testing of EPS-2 cS: Relation of user-perceived response time to error measurement ml: The generation of random, binary, unordered trees m2: The intersection graph of paths in trees m3: Graph minors IV: Widths of trees and well-quasi-ordering m4: Graph minors: A survey In this example, a file of text objects consists of nine titles of technical documents with titles cl-cS concerned with human/computer interaction and titles ml-m4 concerned with mathematical graph theory. In Table 1, words occurring in more than one 30 title are italicized. Using conventional keyword retrieval, if a user requested papers dealing with "human compu~er interaction," titles c1, c2, and c4 would be returned, since these titles 6;~
contain at least one keyword from the user request. However, c3 and cS, while related to the query, would not be returned since they share no words in cs~mmon with the request. It is now shown how latent semantic structure analysis treats this request to return titles c3 and cS.
Table 2 depicts the l'term-by-document" matrix for the 9 technical document titles. Eaeh cell entry, (i,j), is the frequeDey of occurrence of term i in document j. This basic term-by-document matrix or a mathematical transformation thereof is used as input to the statistical procedure described below.
10 TERMS DO~UMENT~
cl c2 c3 c4 cS ml m2 m3 m4 human 1 0 0 1 0 0 0 0 0 interface 1 0 1 0 0 0 0 0 0 15 computer 1 1 0 0 0 0 0 0 0 user 0 1 1 0 1 0 0 0 0 system 0 1 1 2 0 0 0 0 0 response 0 1 0 0 1 0 0 0 0 time 0 1 0 0 1 0 0 0 0 survey 0 1 0 0 0 0 0 0 tree 0 0 0 û O 1 1 1 0 graph O O O O O 0 minor 0 0 0 0 0 0 0 For this example the documents and terms have been carefully selected to yield a good approximatiDn in just two dimensions for expository purposes. FIG. 1 is a two dimensional graphical representation of the two largest dimensions resulting from the statistical process, singular value decomposition. Both document titles and the terms used in them are fit into the same space. Terms are shown as circles and labeled by number.
30 Document titles are represented by squares with the numbers of constituent terms indicated parenthetically. The cosiIIe or dot product between two objects (terms or documents) describe their estimated similarity. In this rep}esentation, the two types of documents form two distinct groups: all the mathematical graph theory titles occupy the same region in ~L~0~i~6~
space (basically along Dimension 1 of FIG. 1,) whereas a quite distinct group is formed for human/computer interaction titles (essentially along Dimension 2 of FIG. 1).
To respond to a user query about "human compllter interaction," the query is first folded into this two-dimeDsional space using those query terms that occur in 5 the space (namely, "human" and "computer"). The query vector is located in the direction of the weighted average of these constituent terms, and is denoted by a directional arrow labeled "Q" in FIG. 1. A measure of closeness or similarity is related to the angle between the query vector and any given term or document vector. One such measure is the cosine between the query vector and a given term or document vector. In FIG. 1 the cosine 10 between the query vector and each c1-cS titles is greater than 0.90; the angle corresponding to the cosine value of 0.90 with the query is shown by the dashed lines in FIG. 1. With this technique, documents c3 and cS would be returned as matches to the user query, even though they share no common terms with the qllery. This is because the latent semantic structure (represented in FIG. 1) fits the overall pattern of term usage across documents.
Descr)p~ion of Singular Value Decomp~s~t~on To obtain the data to plot FIG. 1, the "term-by-document" matrix of Table 2 is decomposed using singular value decomposition (SVD). A reduced SVD isemployed to approximate the original matrix in terms of a much smaller number oforthogonal dimensions. This reduced SVD is used for retrieval; it describes major 20 associational structures in the matrix but it ignores small variations in word usage. The number of dimensions to represent adequately a particular domain is largely an empirical matter. If the number of dimensions is too large, random noise or variations in ~ord usage will be modeled. If the number of dimensions is too small, significant semantic content will remain uncaptured. ~or diverse information sources, 100 or more dimensions may be 25 needed.
To illustrate the decomposition technique, the term-by-document matrix, denoted Y, is decomposed into three other matrices, namely, the term matrix (TERM), the document matrix (DOCUMENT)~ and a diagonal matrix of singular values(DIAGONAL), as follows:
t,d TERl!v[t,m DIAGONALm m DOCUMENTl'm d where Y is the original t-by-d matrix, TERM is the t-by-m matrix that has unit-length orthogonal columns, DOCUMEiNT~ is the transpose of the d-by-m DOCUMENT matrix with unit-length orthogoDal columns, and DIAGONAL is the m-by-m diagonal matrix of singular values typically ordered by magnitude.
~3~ 6~
Table 2 depicts the l'term-by-document" matrix for the 9 technical document titles. Eaeh cell entry, (i,j), is the frequeDey of occurrence of term i in document j. This basic term-by-document matrix or a mathematical transformation thereof is used as input to the statistical procedure described below.
10 TERMS DO~UMENT~
cl c2 c3 c4 cS ml m2 m3 m4 human 1 0 0 1 0 0 0 0 0 interface 1 0 1 0 0 0 0 0 0 15 computer 1 1 0 0 0 0 0 0 0 user 0 1 1 0 1 0 0 0 0 system 0 1 1 2 0 0 0 0 0 response 0 1 0 0 1 0 0 0 0 time 0 1 0 0 1 0 0 0 0 survey 0 1 0 0 0 0 0 0 tree 0 0 0 û O 1 1 1 0 graph O O O O O 0 minor 0 0 0 0 0 0 0 For this example the documents and terms have been carefully selected to yield a good approximatiDn in just two dimensions for expository purposes. FIG. 1 is a two dimensional graphical representation of the two largest dimensions resulting from the statistical process, singular value decomposition. Both document titles and the terms used in them are fit into the same space. Terms are shown as circles and labeled by number.
30 Document titles are represented by squares with the numbers of constituent terms indicated parenthetically. The cosiIIe or dot product between two objects (terms or documents) describe their estimated similarity. In this rep}esentation, the two types of documents form two distinct groups: all the mathematical graph theory titles occupy the same region in ~L~0~i~6~
space (basically along Dimension 1 of FIG. 1,) whereas a quite distinct group is formed for human/computer interaction titles (essentially along Dimension 2 of FIG. 1).
To respond to a user query about "human compllter interaction," the query is first folded into this two-dimeDsional space using those query terms that occur in 5 the space (namely, "human" and "computer"). The query vector is located in the direction of the weighted average of these constituent terms, and is denoted by a directional arrow labeled "Q" in FIG. 1. A measure of closeness or similarity is related to the angle between the query vector and any given term or document vector. One such measure is the cosine between the query vector and a given term or document vector. In FIG. 1 the cosine 10 between the query vector and each c1-cS titles is greater than 0.90; the angle corresponding to the cosine value of 0.90 with the query is shown by the dashed lines in FIG. 1. With this technique, documents c3 and cS would be returned as matches to the user query, even though they share no common terms with the qllery. This is because the latent semantic structure (represented in FIG. 1) fits the overall pattern of term usage across documents.
Descr)p~ion of Singular Value Decomp~s~t~on To obtain the data to plot FIG. 1, the "term-by-document" matrix of Table 2 is decomposed using singular value decomposition (SVD). A reduced SVD isemployed to approximate the original matrix in terms of a much smaller number oforthogonal dimensions. This reduced SVD is used for retrieval; it describes major 20 associational structures in the matrix but it ignores small variations in word usage. The number of dimensions to represent adequately a particular domain is largely an empirical matter. If the number of dimensions is too large, random noise or variations in ~ord usage will be modeled. If the number of dimensions is too small, significant semantic content will remain uncaptured. ~or diverse information sources, 100 or more dimensions may be 25 needed.
To illustrate the decomposition technique, the term-by-document matrix, denoted Y, is decomposed into three other matrices, namely, the term matrix (TERM), the document matrix (DOCUMENT)~ and a diagonal matrix of singular values(DIAGONAL), as follows:
t,d TERl!v[t,m DIAGONALm m DOCUMENTl'm d where Y is the original t-by-d matrix, TERM is the t-by-m matrix that has unit-length orthogonal columns, DOCUMEiNT~ is the transpose of the d-by-m DOCUMENT matrix with unit-length orthogoDal columns, and DIAGONAL is the m-by-m diagonal matrix of singular values typically ordered by magnitude.
~3~ 6~
The dimensionality of the full solution, denoted m, is the rank of the t-by-d matrix, that is, m ~; min(t,d). Tables 3, 4 aDd S below show the TE2M and DOCUMFNT matrices and the diagonal elements of the DIAGONAL matrix, respectively, as fou~d via SVD.
T~BLI~ 3 S TE~M MATRI2~ (12 terms by 9 dimen~ions) human 0.22 -0.11 0.29 -0.41 -0.11 -0.34 -.52 -0.06 -0.41 interface 0.20 -0.07 0.14 -0.55 0.28 0.50 -0.07 -0.01 -0.11 computer 0.24 0.04 -0.16 -0.59 -0.11 -0.2S -0.30 0.06 0.49 10 user 0.40 0.06 -0.34 D.10 0.33 0.38 0.00 0.00 0.01 systern 0.64 -0.17 0.36 0.33 -0.16 -0.21 -0.16 0.03 0.27 response 0.26 0.11 -0.42 0.07 0.08 -0.17 0.28 -0.02 -0.05 time 0.26 0.11 -0.42 0.07 0.08 -0.17 0.28 -0.02 -0.05 EPS 0.30 -0.14 0.33 0.19 0.11 0.27 0.03 -0.02 -0.16 15 survey 0.20 0.27 -0.18 -0.03 -0.54 0.08 -0.47 -0.04 -0.58 tree 0.01 0.4S 0.23 0.02 0.59 -0.39 -0.29 0.25 -0.22 graph 0.04 0.62 0.22 0.00 -0.07 0.11 0.16 -0.68 0.23 minor 0.03 0.45 0.14 -0.01 -0.30 0.28 0.34 0.68 0.18 ~3~
T~BLI~ 3 S TE~M MATRI2~ (12 terms by 9 dimen~ions) human 0.22 -0.11 0.29 -0.41 -0.11 -0.34 -.52 -0.06 -0.41 interface 0.20 -0.07 0.14 -0.55 0.28 0.50 -0.07 -0.01 -0.11 computer 0.24 0.04 -0.16 -0.59 -0.11 -0.2S -0.30 0.06 0.49 10 user 0.40 0.06 -0.34 D.10 0.33 0.38 0.00 0.00 0.01 systern 0.64 -0.17 0.36 0.33 -0.16 -0.21 -0.16 0.03 0.27 response 0.26 0.11 -0.42 0.07 0.08 -0.17 0.28 -0.02 -0.05 time 0.26 0.11 -0.42 0.07 0.08 -0.17 0.28 -0.02 -0.05 EPS 0.30 -0.14 0.33 0.19 0.11 0.27 0.03 -0.02 -0.16 15 survey 0.20 0.27 -0.18 -0.03 -0.54 0.08 -0.47 -0.04 -0.58 tree 0.01 0.4S 0.23 0.02 0.59 -0.39 -0.29 0.25 -0.22 graph 0.04 0.62 0.22 0.00 -0.07 0.11 0.16 -0.68 0.23 minor 0.03 0.45 0.14 -0.01 -0.30 0.28 0.34 0.68 0.18 ~3~
DOCUMENT MAT~IX (9 documents by 9 dimensions) -c1 0.20 -0.06 0.11 -0.95 0.04 -0.08 0.18-0.01 -0.06 c2 0.60 0.16 -0.50 -0.~3 -0.21 -0.02-0.43 0.05 0.2~
c3 0.~6 -0.13 0.21 0.04 0.38 0.07 -0.240.01 0.02 c4 0.54 -0.23 0.57 0.27 -0.20 -0.04 0.2S-0.02 -0.08 cS 0.28 0.11 -0.50 0.15 0.33 0.03 0.67-0.06 -0.26 ml 0.00 0.19 0.10 0.02 0.39 -0.30 -0.340.45 -0.62 m2 0.01 0.44 0.19 0.02 0.35 -0.21-0.15 -û.76 0.02 m3 0.02 0.62 0.25 0.01 0.15 0.000.25 0.45 0.S2 m4 0.08 0.53 0.08 -0.02 -0.60 0.360.04 -0.07 -0.45 DIAGONAL (9 singul~r vallles) ~
3.34 2.54 2.35 1.64 1.50 1.31 0.84 0.56 0.36 As alluded to ea}lier, data to plot FIG. 1 was obtained by presuming that two-dimensions are sufficient to capture the major associational structure of the t-by-d matrix, that is, m is set to two in the e~pression for Yt d~ yielding an approximation of the 20 original matrL~. Only the first two columns of the TERM and DOCUMENT matrices are considered with the remaining columns being igDored. Thus, the term data point corresponding to "human" in FIC;. 1 is plotted with coordinates (0.22,-0.11), which are extracted from the first row and the two left-most columns of the TERM matrix. Similarly, the document data point corresponding to title ml has cooIdinates (0.00,0.19), coming from 25 row six and the two left-most columns of the DOCUMENT matrix.
General ~odel Details It is now elucidating to describe in somewhat more detail the mathematical model underlying the latent structure, singular value decomposition technique.
606~:
c3 0.~6 -0.13 0.21 0.04 0.38 0.07 -0.240.01 0.02 c4 0.54 -0.23 0.57 0.27 -0.20 -0.04 0.2S-0.02 -0.08 cS 0.28 0.11 -0.50 0.15 0.33 0.03 0.67-0.06 -0.26 ml 0.00 0.19 0.10 0.02 0.39 -0.30 -0.340.45 -0.62 m2 0.01 0.44 0.19 0.02 0.35 -0.21-0.15 -û.76 0.02 m3 0.02 0.62 0.25 0.01 0.15 0.000.25 0.45 0.S2 m4 0.08 0.53 0.08 -0.02 -0.60 0.360.04 -0.07 -0.45 DIAGONAL (9 singul~r vallles) ~
3.34 2.54 2.35 1.64 1.50 1.31 0.84 0.56 0.36 As alluded to ea}lier, data to plot FIG. 1 was obtained by presuming that two-dimensions are sufficient to capture the major associational structure of the t-by-d matrix, that is, m is set to two in the e~pression for Yt d~ yielding an approximation of the 20 original matrL~. Only the first two columns of the TERM and DOCUMENT matrices are considered with the remaining columns being igDored. Thus, the term data point corresponding to "human" in FIC;. 1 is plotted with coordinates (0.22,-0.11), which are extracted from the first row and the two left-most columns of the TERM matrix. Similarly, the document data point corresponding to title ml has cooIdinates (0.00,0.19), coming from 25 row six and the two left-most columns of the DOCUMENT matrix.
General ~odel Details It is now elucidating to describe in somewhat more detail the mathematical model underlying the latent structure, singular value decomposition technique.
606~:
Any rectangular matrix Y of t rows and d columns, for example, a t-by-d matrix of terms and documeDts, can be decomposed h~to a product of three other matrices:
Y = To SO D O~ (1) 5 such that To and Do have unlt-length orthogonal columns (i.e. To~To = I; DoTDo = I) and SO is diagonal. This is called the singular value decomposition (SVD) of Y. (A procedure for SVD is described in the text ~umerical ~ec~pes, by Press, Flannery, Teukolsky and Vetterling, 1986, Cambridge University Press, Csmbridge, England). To and Do are the matrices of left and right singular vectors and SO is the diagonal matrix of singular values.
10 By convention, the diagonal elements of SO are ordered in decreasing magnitude.
With SVD, It is possible to devise a simple strategy for an optimal approximation to Y using smaller matrices. The k largest singular values and their associated columns in TD and Do may be kept and the remaining entries set to zero. The product of the resulting matrices is a matrix YR which is approximately equal to Y, and is 15 of rank k. The new matri~ Y~ is the matrix of rank k which is the closest in the least squares sense to Y. Since zeros were introduced into SO' the representation of SO can be simplified by deleting the rows and columns having these zeros to obtain a new diagonal matrix S, and then deleting the corresponding columns of To and Do to define new matrices T and D, respectively. The result is a reduced model such that YR = TSDT. (2) The value of k is chosen for each application; it is generally such that k 2100 for collections of 1000-3000 data objects.
For discussion purposes, it is useful to interpret the SVD
geometrically. The rows of the reduced matrices T and D may be taken as vectors 25 representiDg the terms a31d documents, respectively, in a k-dimensional spacs. With appropriate rescaling of the axes, by quantities related to the associated diagonal values of S, dot products between points in the space can be used to access and compare objects. (A
simplified approach which did not involve rescaling was used to plot the data of FIG. 1, but this was strictly for expository purposes.) These techniques are now discussed.
~3060~;~
Fundamental ~omparisons There are basically three types of comparisons of interest: (i) those comparing two terms; (ii) those comparing two doc~lments or text objects; aDd (iii) thosc comparing a term and a document or text object. As used throughout, the notion of a text S object or data object is general whereas a document is a specific instance of a te~t object or data object. Also, text or data objects are stored in the computer system in files.
Two Terms: In the data, the dot product between two row vectors of YR tells the extent to which two terms have a similar pattern of occurrence across the set of documents. The matrix YRYTR is the square symmetric matrix approximation containing all the term-by-10 term dot products. Using equation (2), y yT = (TSDT)(TSDT)T = TS2TT= (TS)(TS) . (3) This means that the dot product between the i-th row and j-th row of YR can be obtained by calculating the dot product between the i-th and j-th rows of the TS matrix. That is, considering the rows of TS as vectors representing the terms, dot products between these 15 vectors give the comparison between the terms. The relation between taking the rows of T
as vectors and those of TS as vectors is simple since S is a diagonal rnatrix; each vector element has been stretched or shrunk by the corresponding element of S.
Tw~ Doc~ments: In this case, the dot product is between two column vectors of Y. The document-to-document dot product is approximated by yTRYR = (TSDT)T(TSDT) = DS2DT = ~Ds)(Ds)T (4) Thus the rows of the DS matri2~ are taken as vectors representing the documents, and the comparison is via the dot product between the rows of the DS matrix.
Term and Document: This comparison is somewhat different. Instead of trying to estimate the dot product betweeD rows or between columns of Y, the Eundamental comparison25 between a term and a document is the value of an individual cell in Y. The approximation of Y is simply equation t2), i.e., YR - TSDT. The i,j cell of YR may therefore be obtained by taking the dot product between the i-th row of the matrix TS1/2 and the j-th row of the matrix DS1/2. While the "within" ~term or document) comparisons involved using rows of TS and DS as vectors, the "between" comparison requires TS1/2 and DS1/2 for 30 coordinates. Thus it is not possible to make a single configuration of points in a space that ~1.3~ 2 will allow both "between" and "withhl" comparisons. Tbey will be similar, however, differing only by a stretching or shrinking of the dimensional elements by a factor S1/2.
Representatfons of Pseudo-Obje~ts The previous results show how it is possible to compute comparisons S between the various objects associated with the ro vs or columns of Y. It is very important in information retrieval applications to compute similar comparison quantities for objects such as queries that do not appear explicitly in Y. For e~ample, it is necessary to be able to take a completely novel query, find a location in the k-dimensional latent semantic space for it, and then evaluate its cosine or inner product with respect to terms or objects in the space.
10 Another example would be trying, after-the-fact, to find representations for documents that did not appear in the original space. The new objects for both these examples are equivalent to objects in the matrix Y in that they may be represented as vectors of terms.
For this reason they are called pseudo-documents specifically or pseudo-objects generically.
In order to compare pseudo-documents to other documents, the starting point is defining a 15 pseudo-document vector, designated Yq. Then a representation Dq is derived such that Dq can be used just like a row of D in the comparison relationships described in the foregoing sections. One criterion for such a derivation is that the insertion of a real document Yi should give Di when the model is ideal (i.e., Y=YR). With this constraint, Y = TSD 1' q q 20 or, smce I T equals the identlty matri~, D T S-lTTY
or, finally, D = yT TS-l. (5) Thus, with appropriate rescaling of the a~es, this amounts to placing the pseudo-object at 25 the vector sum of its corresponding term points. Then Dq may be used like any row of D
and, appropriately scaled by S or S1/2, can be used like a usual document vector for making "withiD" and "between" comparisons. It is to be noted that if the measure of similarity to be used in comparing the query against all the documents is one in which only the angle between the vectors is important (such as the cosin0), there is no difference for comparison :ll3~
11 ~
purposes betveen placing the query at the vector average or the vector sum of its terms.
3~rativ~ Embodiment The foundation principles presented in the foregoing sections are now applied to a practical example by way of teaching an illustrative embodiment in accordance S with the present invention.
The system under consideration is one that receives a request for technical information from a user and returns as a response display the most appropriate groups in a large, technically diverse company dealing with that technical information. The size of each group is from five to ten people. There is no expert who understands iD detail 10 what every group is accomplishing. ~ach person's understanding or knowledge of the company's technical vork tends to be myopic, that is, each one knows their particular group's work, less about neighboring groups and their knowledge becomes less precise or even none~istent as one moves further a~ay from the core group.
~f each group can be described by a set of terms, then the latent 15 semantic indexing procedure can be applied. For instance, one set of textual descriptions might include annual write-ups each group member must prepare in describing the planned activity for the coming year. Another input could be the abstracts of technical memoranda written by members of each group.
The technique for processing the documents gathered together to 20 represent the company technical information is shown in block diagram form in FIG. 2. The first processing activity, as illustrated by processing block 100, is that of text preprocessing.
All the combined text i9 preprocessed to identify terms and possible compound noun phrases. First, phrases are found by identifying all words between (1) a precompiled list of stop words; or (2) punctuation marks, or (3) parenthetical remarks.
To obtain more stable estimates of word frequencies, all inflectional suffixss (past tense, plurals, adverbials, progressive tense, and so forth) are removed from the words. Inflectional suffixes, in contrast to derivational suffixes, are those that do not usually change the meaning of the base word. (For example, removing the "s" from "boys"
does not change the meaning of the base word whereas stripping "ation" from "information"
30 does change the meaning). Since no single set of pattern-action rules can correctly descril~e English language, the suffix stripper sub-program may contain an cxception list.The next step to the processing is represented by block 110 in FIG. 2.
Based upon the earlier text preprocessing, a system le~icon is created. The le~icon includes both single vvord and noun phases. The noun phrases provide for a richer semantic space.
35 For e~ample, the "information" in "information retrieval" and "information theory" have different meanings. Treating these as separate terms places each of the compounds at different places in the k-dimensional syace. (For a word in radically different semantic environments, treating it as a single word tends to place ehe word in a meaningless place in k-dimensional space, whereas trcating each of its different semantic environments separately using separate compounds yields spatial differentiation).
Compound noun phrases may be extracted using a simplifiedJ
automatic procedure. First, phrases are found using the "pseudo" parsing techDique described with respect to step 100. Then all left and right branching subphrases are found.
Any phrase or subphrase that occurs in more than one document is a potential compound phrase. Compound phrases may range from tvo to many words ~e.g., "semi-insulating Fe-10 doped InP current blocking layer"). From these potential compolmd phrases, all longest-matching phrases as well as single words making up the compounds are entered into the lexicon base to obtain spatial separation.
In the illustrative embodiment, all inflectionally stripped single vords occurring in more than one document and that are not on the list of most frequently used 15 words in English (such as "the", "and") are also included in the system lexicon. Typically, the e~clusion list comprises about 150 common words.
~rom the list of lexicon terms, the Term-by-Document matrix is created, as depicted by processing block 120 in FI&. 2. In one exemplary situation, the matrix contained 7100 terms and 728 documents representing 480 groups.
The next step is to perform the singular value decomposition on the Term-by-Document matrix, as depicted by processing block 130. This analysis is only effected once (or each time there is a significant update in the storage files).The last step in processing the documPnts prior to a user query is depicted by block 140. In order to relate a selected document to the group responsible for 25 that document, an organizational database is constructed. This latter database may contain, for instance, the group manager's name and the manager's mail address.
The user query processing activity is depicted on the right-hand side of FIG. 2. The first step, as represented by processing block 200, is to preprocess the query iD
the same way as the original documents.
As then depicted by block 210 the longest matching compound phrases as well as single words not part of compound phrases are extracted from the query. For each query term also contained in the system lexicon, the k-dimensional vector is located.
The query vector is the weighted vector average of the k-dimensional vectors. Processing block 220 depicts the generation step for the query vector.
The next step in the query processing is depicted by processing block 230. In order that the best matching document is located, the query vector is compared to all documents in the space. The similarity metris used is the cosine betveen the query )6~
vector and the document vectors. A cosine of 1.0 would indicate that the query vector and the document vector were on top of one another in the space. The cosine metric is similar to a dot product measure except that it lgnores the magnitude of the vectors and simply uses the angle between the vectors being compared.
The cosines are sorted, as depicted by processiDg block 240, and for each of the best N matching documents (typically N=8), the value of the cosine along with organizational information corresponding to the documentls group are displayed to the user, as depicted by prOCeSSiDg block 250. Table 6 shows a typical input and output for N= 5.
10 INPUT QUERY: An ExpertlExpert-Locating System Based on Automatic Representation of Semantic Structure OUTPUT RESULTS:
1. Group: B
Group Title: Artificial Intelligence and Information Science Research Group Manager: D. E. Walker, Address B, Phone B
Fit (Cosine): 0.67 2. Group: A
Group Title: Artificial Intelligence and Communications Research Group MaDager: L. A. Streeter, Address A, Phone A
Fit (Cosine): 0.64 3. Group: E
Group Title: Cognitive Science Research Group Manager: T. K. Laudauer, Address E, Phone E
Fit (Cosine): 0.63 4. Group: C
Group Title: Experimental Systems Group Manager: C. A. Riley, Address C, Phone C
Fit (Cosine): 0.62 5. Group: D
Group Title: Software Technology Group Manager: C. P. Lewis, Address D, Phone D
Fit (Cosine): 0.55 It is to be further understood that the metbodology described herein is not limited to the specific forms disclosed by way of illustration, but may assume other embodiments limited only by the scope of the appended claims.
Y = To SO D O~ (1) 5 such that To and Do have unlt-length orthogonal columns (i.e. To~To = I; DoTDo = I) and SO is diagonal. This is called the singular value decomposition (SVD) of Y. (A procedure for SVD is described in the text ~umerical ~ec~pes, by Press, Flannery, Teukolsky and Vetterling, 1986, Cambridge University Press, Csmbridge, England). To and Do are the matrices of left and right singular vectors and SO is the diagonal matrix of singular values.
10 By convention, the diagonal elements of SO are ordered in decreasing magnitude.
With SVD, It is possible to devise a simple strategy for an optimal approximation to Y using smaller matrices. The k largest singular values and their associated columns in TD and Do may be kept and the remaining entries set to zero. The product of the resulting matrices is a matrix YR which is approximately equal to Y, and is 15 of rank k. The new matri~ Y~ is the matrix of rank k which is the closest in the least squares sense to Y. Since zeros were introduced into SO' the representation of SO can be simplified by deleting the rows and columns having these zeros to obtain a new diagonal matrix S, and then deleting the corresponding columns of To and Do to define new matrices T and D, respectively. The result is a reduced model such that YR = TSDT. (2) The value of k is chosen for each application; it is generally such that k 2100 for collections of 1000-3000 data objects.
For discussion purposes, it is useful to interpret the SVD
geometrically. The rows of the reduced matrices T and D may be taken as vectors 25 representiDg the terms a31d documents, respectively, in a k-dimensional spacs. With appropriate rescaling of the axes, by quantities related to the associated diagonal values of S, dot products between points in the space can be used to access and compare objects. (A
simplified approach which did not involve rescaling was used to plot the data of FIG. 1, but this was strictly for expository purposes.) These techniques are now discussed.
~3060~;~
Fundamental ~omparisons There are basically three types of comparisons of interest: (i) those comparing two terms; (ii) those comparing two doc~lments or text objects; aDd (iii) thosc comparing a term and a document or text object. As used throughout, the notion of a text S object or data object is general whereas a document is a specific instance of a te~t object or data object. Also, text or data objects are stored in the computer system in files.
Two Terms: In the data, the dot product between two row vectors of YR tells the extent to which two terms have a similar pattern of occurrence across the set of documents. The matrix YRYTR is the square symmetric matrix approximation containing all the term-by-10 term dot products. Using equation (2), y yT = (TSDT)(TSDT)T = TS2TT= (TS)(TS) . (3) This means that the dot product between the i-th row and j-th row of YR can be obtained by calculating the dot product between the i-th and j-th rows of the TS matrix. That is, considering the rows of TS as vectors representing the terms, dot products between these 15 vectors give the comparison between the terms. The relation between taking the rows of T
as vectors and those of TS as vectors is simple since S is a diagonal rnatrix; each vector element has been stretched or shrunk by the corresponding element of S.
Tw~ Doc~ments: In this case, the dot product is between two column vectors of Y. The document-to-document dot product is approximated by yTRYR = (TSDT)T(TSDT) = DS2DT = ~Ds)(Ds)T (4) Thus the rows of the DS matri2~ are taken as vectors representing the documents, and the comparison is via the dot product between the rows of the DS matrix.
Term and Document: This comparison is somewhat different. Instead of trying to estimate the dot product betweeD rows or between columns of Y, the Eundamental comparison25 between a term and a document is the value of an individual cell in Y. The approximation of Y is simply equation t2), i.e., YR - TSDT. The i,j cell of YR may therefore be obtained by taking the dot product between the i-th row of the matrix TS1/2 and the j-th row of the matrix DS1/2. While the "within" ~term or document) comparisons involved using rows of TS and DS as vectors, the "between" comparison requires TS1/2 and DS1/2 for 30 coordinates. Thus it is not possible to make a single configuration of points in a space that ~1.3~ 2 will allow both "between" and "withhl" comparisons. Tbey will be similar, however, differing only by a stretching or shrinking of the dimensional elements by a factor S1/2.
Representatfons of Pseudo-Obje~ts The previous results show how it is possible to compute comparisons S between the various objects associated with the ro vs or columns of Y. It is very important in information retrieval applications to compute similar comparison quantities for objects such as queries that do not appear explicitly in Y. For e~ample, it is necessary to be able to take a completely novel query, find a location in the k-dimensional latent semantic space for it, and then evaluate its cosine or inner product with respect to terms or objects in the space.
10 Another example would be trying, after-the-fact, to find representations for documents that did not appear in the original space. The new objects for both these examples are equivalent to objects in the matrix Y in that they may be represented as vectors of terms.
For this reason they are called pseudo-documents specifically or pseudo-objects generically.
In order to compare pseudo-documents to other documents, the starting point is defining a 15 pseudo-document vector, designated Yq. Then a representation Dq is derived such that Dq can be used just like a row of D in the comparison relationships described in the foregoing sections. One criterion for such a derivation is that the insertion of a real document Yi should give Di when the model is ideal (i.e., Y=YR). With this constraint, Y = TSD 1' q q 20 or, smce I T equals the identlty matri~, D T S-lTTY
or, finally, D = yT TS-l. (5) Thus, with appropriate rescaling of the a~es, this amounts to placing the pseudo-object at 25 the vector sum of its corresponding term points. Then Dq may be used like any row of D
and, appropriately scaled by S or S1/2, can be used like a usual document vector for making "withiD" and "between" comparisons. It is to be noted that if the measure of similarity to be used in comparing the query against all the documents is one in which only the angle between the vectors is important (such as the cosin0), there is no difference for comparison :ll3~
11 ~
purposes betveen placing the query at the vector average or the vector sum of its terms.
3~rativ~ Embodiment The foundation principles presented in the foregoing sections are now applied to a practical example by way of teaching an illustrative embodiment in accordance S with the present invention.
The system under consideration is one that receives a request for technical information from a user and returns as a response display the most appropriate groups in a large, technically diverse company dealing with that technical information. The size of each group is from five to ten people. There is no expert who understands iD detail 10 what every group is accomplishing. ~ach person's understanding or knowledge of the company's technical vork tends to be myopic, that is, each one knows their particular group's work, less about neighboring groups and their knowledge becomes less precise or even none~istent as one moves further a~ay from the core group.
~f each group can be described by a set of terms, then the latent 15 semantic indexing procedure can be applied. For instance, one set of textual descriptions might include annual write-ups each group member must prepare in describing the planned activity for the coming year. Another input could be the abstracts of technical memoranda written by members of each group.
The technique for processing the documents gathered together to 20 represent the company technical information is shown in block diagram form in FIG. 2. The first processing activity, as illustrated by processing block 100, is that of text preprocessing.
All the combined text i9 preprocessed to identify terms and possible compound noun phrases. First, phrases are found by identifying all words between (1) a precompiled list of stop words; or (2) punctuation marks, or (3) parenthetical remarks.
To obtain more stable estimates of word frequencies, all inflectional suffixss (past tense, plurals, adverbials, progressive tense, and so forth) are removed from the words. Inflectional suffixes, in contrast to derivational suffixes, are those that do not usually change the meaning of the base word. (For example, removing the "s" from "boys"
does not change the meaning of the base word whereas stripping "ation" from "information"
30 does change the meaning). Since no single set of pattern-action rules can correctly descril~e English language, the suffix stripper sub-program may contain an cxception list.The next step to the processing is represented by block 110 in FIG. 2.
Based upon the earlier text preprocessing, a system le~icon is created. The le~icon includes both single vvord and noun phases. The noun phrases provide for a richer semantic space.
35 For e~ample, the "information" in "information retrieval" and "information theory" have different meanings. Treating these as separate terms places each of the compounds at different places in the k-dimensional syace. (For a word in radically different semantic environments, treating it as a single word tends to place ehe word in a meaningless place in k-dimensional space, whereas trcating each of its different semantic environments separately using separate compounds yields spatial differentiation).
Compound noun phrases may be extracted using a simplifiedJ
automatic procedure. First, phrases are found using the "pseudo" parsing techDique described with respect to step 100. Then all left and right branching subphrases are found.
Any phrase or subphrase that occurs in more than one document is a potential compound phrase. Compound phrases may range from tvo to many words ~e.g., "semi-insulating Fe-10 doped InP current blocking layer"). From these potential compolmd phrases, all longest-matching phrases as well as single words making up the compounds are entered into the lexicon base to obtain spatial separation.
In the illustrative embodiment, all inflectionally stripped single vords occurring in more than one document and that are not on the list of most frequently used 15 words in English (such as "the", "and") are also included in the system lexicon. Typically, the e~clusion list comprises about 150 common words.
~rom the list of lexicon terms, the Term-by-Document matrix is created, as depicted by processing block 120 in FI&. 2. In one exemplary situation, the matrix contained 7100 terms and 728 documents representing 480 groups.
The next step is to perform the singular value decomposition on the Term-by-Document matrix, as depicted by processing block 130. This analysis is only effected once (or each time there is a significant update in the storage files).The last step in processing the documPnts prior to a user query is depicted by block 140. In order to relate a selected document to the group responsible for 25 that document, an organizational database is constructed. This latter database may contain, for instance, the group manager's name and the manager's mail address.
The user query processing activity is depicted on the right-hand side of FIG. 2. The first step, as represented by processing block 200, is to preprocess the query iD
the same way as the original documents.
As then depicted by block 210 the longest matching compound phrases as well as single words not part of compound phrases are extracted from the query. For each query term also contained in the system lexicon, the k-dimensional vector is located.
The query vector is the weighted vector average of the k-dimensional vectors. Processing block 220 depicts the generation step for the query vector.
The next step in the query processing is depicted by processing block 230. In order that the best matching document is located, the query vector is compared to all documents in the space. The similarity metris used is the cosine betveen the query )6~
vector and the document vectors. A cosine of 1.0 would indicate that the query vector and the document vector were on top of one another in the space. The cosine metric is similar to a dot product measure except that it lgnores the magnitude of the vectors and simply uses the angle between the vectors being compared.
The cosines are sorted, as depicted by processiDg block 240, and for each of the best N matching documents (typically N=8), the value of the cosine along with organizational information corresponding to the documentls group are displayed to the user, as depicted by prOCeSSiDg block 250. Table 6 shows a typical input and output for N= 5.
10 INPUT QUERY: An ExpertlExpert-Locating System Based on Automatic Representation of Semantic Structure OUTPUT RESULTS:
1. Group: B
Group Title: Artificial Intelligence and Information Science Research Group Manager: D. E. Walker, Address B, Phone B
Fit (Cosine): 0.67 2. Group: A
Group Title: Artificial Intelligence and Communications Research Group MaDager: L. A. Streeter, Address A, Phone A
Fit (Cosine): 0.64 3. Group: E
Group Title: Cognitive Science Research Group Manager: T. K. Laudauer, Address E, Phone E
Fit (Cosine): 0.63 4. Group: C
Group Title: Experimental Systems Group Manager: C. A. Riley, Address C, Phone C
Fit (Cosine): 0.62 5. Group: D
Group Title: Software Technology Group Manager: C. P. Lewis, Address D, Phone D
Fit (Cosine): 0.55 It is to be further understood that the metbodology described herein is not limited to the specific forms disclosed by way of illustration, but may assume other embodiments limited only by the scope of the appended claims.
Claims (11)
1. An information retrieval method comprising the steps of generating term-by-data object matrix data to represent information files stored in a computer system, said matrix data being indicative of the frequency of occurrence of selected terms contained in the data objects stored in the information files, decomposing said matrix into a reduced singular value representation composed of distinct term and data object files, in response to a user query, generating a pseudo-object utilizing said selected terms and inserting said pseudo-object into said matrix data, and examining the similarity between said pseudo-object and said term and data object files to generate an information response and storing said response in the system in a form accessible by the user.
2. The method as recited in claim 1 wherein said step of generating said matrix data includes the step of producing a lexicon database defining said selected terms.
3. The method as recited in claim 2 wherein said step of producing said lexicon database includes the step of parsing the data objects.
4. The method as recited in claim 3 wherein said step of parsing includes the steps of removing inflectional suffixes and isolating phrases in the data objects.
5. The method as recited in claim 2 wherein said step of generating said pseudo-object includes the step of parsing said pseudo-object with reference to said lexicon database.
6. The method as recited in claim 1 further including the step of generating an organizational database associated with the authorship of the data objects and storing said organizational database in the system and said response includes information from said organizational database based on said similarity.
7. The method as recited in claim 1 wherein said matrix database is expressed as Y, said step of decomposing produces said representation in the form Y=T0S0DT0 of rank m, and an approximation representation YR=TSDT of rank k < m, where T0 and D0 represent said term and data object databases and S0 corresponds to said singular value representation and where T, D and S represent reduced forms of T0, D0 and S0, respectively, said pseudo-object is expressible as Yq and said step of inserting includes the step of computing Dq=YqTTS-1, and said step of examining includes the step of evaluating the dot products between said pseudo-object and said term and document matrices.
8. The method as recited in claim 7 wherein the degree of similarity is measured by said dot products exceeding a predetermined threshold.
9. The method as recited in claim 8 wherein said approximation representation is obtained by setting (k+1) through m diagonal values of S0 to zero.
10. The method as recited in claim 1 wherein said matrix database is expressed as Y, said step of decomposing produces said representation in the form Y=T0S0DT0 of rank m, and an approximation representation YR=TSDT of rank k < m, where T0 and D0 represent said term and data object databases and S0 corresponds to said singular value representation and where T, D and S represent reduced forms of T0, D0 and S0, respectively, said pseudo-object is expressible as Yq and said step of inserting includes the step of computing Dq=YqTTS-1, and said step of examining includes the step of evaluating the cosines between said pseudo-object and said term and document matrices.
11. A method for retrieving information from an information file stored in a computer system comprising the steps of generating term by-data object matrix data by processing the information file, performing a singular value decomposition on said matrix data to obtain the reduced term and data object vectors and diagonal values, in response to a user query, generating a pseudo-object vector and augmenting said matrix data with said pseudo-vector using reduced forms of said term vector and said diagonal values and storing said augmented data in the system, and examining the similarities between said pseudo-object vector and said reduced term vector and a reduced form of said data object vector to generate the information and storing the information in a response file accessible to the user.
Applications Claiming Priority (2)
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|---|---|---|---|
| US07/244,349 | 1988-09-15 | ||
| US07/244,349 US4839853A (en) | 1988-09-15 | 1988-09-15 | Computer information retrieval using latent semantic structure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA1306062C true CA1306062C (en) | 1992-08-04 |
Family
ID=22922358
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|---|---|---|---|
| CA000596524A Expired - Lifetime CA1306062C (en) | 1988-09-15 | 1989-04-12 | Computer information retrieval using latent semantic structure |
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| CA (1) | CA1306062C (en) |
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