|6115683||Automatic essay scoring system using content-based techniques||2000-09-05||Burstein et al.||704/1|
|5987446||Searching large collections of text using multiple search engines concurrently||1999-11-16||Corey et al.||707/3|
|5794178||Visualization of information using graphical representations of context vector based relationships and attributes||1998-08-11||Caid et al.||704/9|
|5619709||System and method of context vector generation and retrieval||1997-04-08||Caid et al.||707/532|
|5418948||Concept matching of natural language queries with a database of document concepts||1995-05-23||Turtle||707/4|
|5371807||Method and apparatus for text classification||1994-12-06||Register et al.||382/159|
|5325298||Methods for generating or revising context vectors for a plurality of word stems||1994-06-28||Gallant||704/9|
|5317507||Method for document retrieval and for word sense disambiguation using neural networks||1994-05-31||Gallant||707/532|
|5301109||Computerized cross-language document retrieval using latent semantic indexing||1994-04-05||Landauer et al.||704/9|
|5259766||Method and system for interactive computer science testing, anaylsis and feedback||1993-11-09||Sack et al.||434/362|
|4978305||Free response test grading method||1990-12-18||Kraft||434/353|
|4930077||Information processing expert system for text analysis and predicting public opinion based information available to the public||1990-05-29||Fan||704/8|
|4839853||Computer information retrieval using latent semantic structure||1989-06-13||Deerwester et al.||707/5|
|4773009||Method and apparatus for text analysis||1988-09-20||Kucera et al.||707/531|
1. Field of the Invention
This invention relates to methods that compute the quality of a text that purports to convey information on a topic by computing the amount of relevant subject-matter contained in the sample text and by computing the similarity of that subject-matter information to that contained in at least one standard reference text.
2. Description of Related Art
A need exists, in the educational field, to automate the process of evaluating the quality of essays. No automated process exists for providing quick evaluation of a student's subject matter knowledge and ability to articulate that knowledge in writing. An essay tests the depth of the writer's subject-matter knowledge, and ability to express ideas, but currently no automatic process exists for a quick and through evaluation of the essay.
Currently, the process of grading an essay with regard to quality and quantity of subject matter information and semantic coherence is labor intensive, tedious, and subjective. Whether the essay is written for an in-class project, in-dass test, during a nation-wide admissions test or an assessment test, each person grading an essay spends time analyzing the quality and quantity of subject matter content. Often two graders will compute different scores for the same essay. A need exists for a fair and objective method to grade essays, but the method must also reduce the amount of time spent grading the essay. In addition, this method must be as reliable and accurate as a grade a teacher, professional exam grader, or subject matter expert would assign to the text.
Related automated systems evaluate text with respect to spelling, grammar, sentence length, number of sentences within a paragraph, frequency of words, and total number of words written. These systems do not evaluate the text based on the amount and relevance of subject matter information, and coherence of the writing. Sentences within a paragraph can be grammatically correct, but these sentences can fail to answer the question presented or follow any coherent structure. In addition, these systems do not diagnose and provide information indicating which components of subject matter the essay should or should not contain, and indicate the weak areas in an essay.
Several automated systems use natural language queries for document retrieval. These systems match the words or phrases within the query to documents within a database. The query result lists in descending order the name of documents that contain the greatest number or some other function of the combination of words within the query. Other automated document retrieval systems develop information based on word proximity and importance of the word. If different words are used in query and text, these automated systems cannot evaluate the text based on the amount and relevance of subject matter information, without the addition of intellectually created thesauri, or semantic routines.
Latent Semantic Indexing (LSI) is well known in the art. U.S. Pat. No. 4,839,853, issued on Jun. 13, 1989 to Deerwester et al., and U.S. Pat. No. 5,301,109, issued on Apr. 5, 1994 to Landauer et al, utilize Latent Semantic Indexing to model the underlying correlational structure of the distribution of terms in documents. Computer-based document retrieval and multi-language retrieval systems utilize this process to statistically analyze a text database and an information query and estimate the correlation of topical meaning between words in the query and documents within a database. The '853 and '109 patents present a detailed discussion of the mathematics underlying the LSI techniques. There is no teaching in the art regarding the application of LSI to evaluate the relevant quality and quantity of semantic-content or the coherence of a writing.
This present invention solves the above and other problems, thereby advancing the useful arts, by providing an automated method for analysis and evaluation of semantic content of text. This method does not utilize conventional expert systems, artificial intelligence, or natural language processing. Instead, this method uses statistical analysis, hereinafter Latent Semantic Analysis (LSA) (based in part on known LSI methods), to analyze a corpus of representative text and thereafter to evaluate a sample text, such as an essay. This method assigns a numerical score or grade to a sample text. This score is based on the quality, quantity and relevance of subject matter content in the sample text as compared to one or more standard references, and in some cases aspects of the coherence of the writing.
This invention presumes there is an underlying latent semantic structure in the usage of words that is not easily evident due to the variety of words in our vocabulary that have similar meanings. The variety of words in our vocabulary gives the writer great flexibility to express concepts. The words the writer chooses depends upon the writer's experience or education. Consequently, two writers may choose different words to describe similar information on a particular subject matter topic. This latent semantic structure can be expressed mathematically. A mathematical relation can be computed to determine the relevant variety of words that express a particular topic.
LSA uses a data matrix comprising words and passages of sufficient length to express a full idea. LSA statistically analyzes this body of information in the data matrix to mathematically determine direct and indirect relationships between the words and the various contexts of the words. The statistical analysis provides a trained set of matrices from which all sample texts can be compared to estimate the quality, quantity, and relevance of subject matter content in the each sample text. Additionally, the coherence of the writing in a sample text is also estimated using LSA.
LSA ignores the order of words within the passage, and defines the meaning of a passage as the average of the vectors of the words it contains. Specifically, LSA computes the sum of each word in each vector representing the passage then computes the average of this value. The meaning of the passage can be similarly computed as the sum of the vectors of the words the passage contains. This definition represents the passage as a single vector. The passage can be a phrase, a paragraph, a portion of a text, or the whole text.
Specifically, this invention uses a set of reference documents to create a data matrix which defines the domain of knowledge. Reference documents, for example, can be authoritative text on the subject-matter, the text used to learn the subject-matter, articles on the subject-matter, or essays written by fellow students.
A data matrix is created from unique terms used in two or more reference documents. Terms are referred to as text objects and are used to form rows, the first dimension, within the data matrix. Text objects are unique words, concepts, or phrases. In the data matrix, each element of the text object vector represents of the number of times the text object is used in two or more reference documents.
A segment vector represents the individual reference documents. The segment vector is used to form columns, the second dimension, within the data matrix. A segment vector, for example, can be an entire reference text, abstract of a reference text, title of a reference text, at least one paragraph of a reference text, at least one sentence of a reference text, or a collection of text objects that convey an idea or summarize a topic. Each reference text is associated with a single segment vector within the data matrix.
The intersection of a row and column, a matrix cell, contains the number of times a particular text object appears in a particular segment. A weighted value is applied to each cell value. The resulting cell is a proportional representation of the importance of the cells original information, for example, rare words are weighted more heavily.
Singular value decomposition is applied to the data matrix to decompose the data matrix into three trained matrices. The first trained matrix consists of the original row (text object) identities and columns of orthogonal derived factor values. The second trained matrix consists of the original column (segment) identities and rows of orthogonal derived factor values. The third trained matrix consists of a diagonal matrix containing scaling values. The number of dimensions in these trained matrices is then reduced by setting the smallest values in the scaling matrix to zero. Applying matrix multiplication to these three matrices provides a reduced dimension semantic-space matrix.
Next, the method generates a vector representation of a selected reference text from the plurality of reference text used to create the data matrix. This selected reference text is otherwise known as a standard reference text or is equivalently known as a standard text. The standard reference text is used as a basis of comparison for the ungraded sample text. An average or sum of the text object vectors is computed using each text object within the standard reference text to generate a vector representation of the standard reference text.
The standard reference text may be used along with the plurality of reference texts to create the data matrix. Or, the standard reference text can be read, stored, and parsed after the trained matrices are created.
The student's essay, otherwise known as the ungraded sample text, is parsed into text objects. Next, a pseudo-object vector representation is generated by computing a vector representation of the ungraded sample text.
A pseudo-object vector representation can be computed using two methods. A first method uses the computed semantic-space vector to compute the average of the vector elements the ungraded sample text contains. In an alternative method, the three trained matrices are used to compute the pseudo-object for the ungraded sample text. In this method a vector is computed such that it can be used as any row in the second trained matrix, that is, the document matrix.
The computed pseudo-object of the ungraded sample text is compared against the vector representation of the standard text. The cosine between the pseudo-object vector representation and the vector representation of the standard reference text determines the similarity between both documents. An alternative method to compute the similarity between both documents, comprises computing a dot product between the pseudo-object vector representation and the vector representation of the standard reference text.
The pseudo-object is also used to determine the amount of subject-matter in the student's essay. The amount of knowledge contained in the student's essay is computed as the vector length of the pseudo-object.
To determine the coherence of the student's essay, the essay is divided into portions and a pseudo-object is computed for each portion of the essay. The cosine between the vector representing a first portion and the vector representing the following portion is a measure of the coherence between portions.
This invention provides several features and advantages. One advantage of the present invention is the automation of grading essays with regard to quality and quantity of subject matter information and semantic coherence, thus, reducing the amount of time spent analyzing the quality of information content, and coherence of the writing. Additionally, the essays are graded in a fair and objective manner because the process utilizes the same process for each essay.
Another feature of the present invention provides quick evaluation of a writers subject-matter knowledge and ability to articulate the subject-matter. The automated process allows a testing facility to grade multiple essays written for national essay exams, or computer-based interactive learning situations in less time. An automated grading process diagnoses and provides information indicating which components of subject matter the essay should or should not contain, indicates the weak areas in an essay, and identifies the paragraphs and sentences in the text which are not on topic.
This invention also provides a grade as reliable and accurate as a grade a teacher, professional exam grader, or subject matter expert would assign to the text. Experiments indicate this automated process predicts comparable grades for carefully constructed objective tests, as accurately as those received from human graders. These results are not attributable to how many technical or unusual words the writer uses.
The above and other benefits, aspects, features, and advantages of the present invention will become apparent from the following description and the attached drawings.
While the invention is susceptible to various modifications and alternative forms, a specific embodiment thereof has been shown by way of example in the drawings and will herein be described in detail. It should be understood, however, that it is not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Illustrative Example of the LSI Method
Before discussing the principles and operational characteristics of this invention in detail, it is helpful to present a motivating example of latent semantic analysis. This also aids in introducing terminology utilized later in the discussion.
The contents of Table 1 are used to illustrate how semantic structure analysis works.
|Document Set Based on Titles|
|c1||Human machine interface for Lab ABC computer applications|
|c2||A survey of user opinion of computer system response time|
|c3||The EPS user interface management system|
|c4||Systems and human systems engineering testing of EPS-2|
|c5||Relation of user-perceived response time to error measurement|
|m1||The generation of random, binary, unordered trees|
|m2||The intersection graph of paths in trees|
|m3||Graph minors IV|
In this example, a file of text objects consists of nine titles of technical documents with titles c
Table 2 depicts the “term-by-document” matrix for the 9 technical document titles. Each cell entry, (i,j), is the frequency 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.
For this example the documents and terms have been carefully selected to yield a good approximation in just two dimensions for expository purposes.
Prepare the Data Matrix for Singular Value Decomposition
A weighted value is applied to each cell within the term-by-document of Table 2 before the data matrix undergoes singular value decomposition. The resulting weighted cells are a proportional representation of the importance of the cell's original information, for example, rare words are weighed more heavily.
The weighted value is, for example, computed as the log (individual cell value +1) divided by the text object's entropy value. The weighting equation is:
where p is the proportion of text object occurrences in the row(i), and is computed as follows:
where n is the number of columns in the matrix.
A person skilled in the art will recognize that other weighting methods may be used to compute a proportional representation of the importance of the cell's information.
Description of Singular Value Decomposition
To obtain the data to plot
A reduced SVD is employed to approximate the original data matrix in terms of a much smaller number of orthogonal dimensions. The three reduced dimensional matrices describe major association structures in the term-document matrix but ignore 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 word usage will be modeled. If the number of dimensions is too small, significant semantic content will remain uncaptured. For diverse information sources, 100 or more dimensions may be 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:
where Y is the original t-by-d matrix, TERM is the t-by-k matrix that has unit-length orthogonal columns, DOCUMENT
The dimensionality of the solution, denoted k, is the rank of the t-by-d matrix, that is, k≦min(t,d). Tables 3, 4 and 5 below show the TERM and DOCUMENT matrices and the diagonal elements of the DIAGONAL matrix, respectively, as found via SVD.
|TERM MATRIX (12 terms by 9 dimensions)|
|DOCUMENT MATRIX (9 documents by 9 dimensions)|
|DIAGONAL (9 singular values)|
As alluded to earlier, data to plot
General Model Details
It is now elucidating to describe in somewhat more detail the mathematical model underlying the latent structure, singular value decomposition technique.
Any rectangular matrix Y of t rows and d columns, for example, a t-by-d matrix of terms and documents, can be decomposed into a product of three other matrices:
such that T
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 T
The value of k is chosen for each application; it is generally such that k≧100 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 representing the terms and documents, respectively, in a k-dimensional space. With appropriate resealing 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 resealing was used to plot the data of
For essay grading, the fundamental comparison of interest is comparing two documents, that is, text segments. The comparison of two documents is the dot product between two column vectors of Y. The document-to-document dot product is approximated by
Thus the rows of the DS matrix are taken as vectors representing the documents, and the comparison is via the dot product between the rows of the DS matrix.
Vector Representations of Pseudo-Objects
The previous results show how it is possible to compute comparisons between the various objects associated with the rows or columns of Y. It is very important in essay scoring applications to compute similar comparison quantities for objects such as essays that do not appear explicitly in Y. For example, it is necessary to be able to take an essay, find a location in the k-dimensional latent semantic space for it, and then evaluate its cosine with respect to reference texts in the space. The new object for this example is 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. The pseudo-object vector representation is the average of the vector elements the text contains.
The following is a discussion to compute a pseudo-object vector representation using the two of the three individual matrices obtained from the SVD. In order to compare pseudo-objects to other documents, the starting point is defining a pseudo-document vector, designated Y
Thus, with appropriate resealing of the axes, this amounts to placing the pseudo-object at the vector sum of its corresponding term points. The Dq may be used like any row of D and, appropriately scaled by S or S
Semantic Analysis and Evaluation of the Content of Writing
To extend the principles of LSI to automate and evaluate text,
Reference text can be, for example, articles on the subject-matter, or a set of authoritative text that students used to learn the subject-matter. A reference text includes any text which assists to define the range of the domain of knowledge.
A person skilled in the art will appreciate text can be in machine-readable form before being retrieved. Otherwise the text can be converted to machine-readable text, for example, by scanning the text, manually typing the text into a database, or reciting the text into a speech-to-text translator.
A person skilled in the art will appreciate this information can be stored, for example, in cache, nonvolatile memory, hard-disk space, CD-ROM, floppy disk, or tape.
The segment vector represents the individual reference documents. A segment vector can be, but is not limited to, an entire reference text, abstract, title of a document, at least one paragraph of a text, at least one sentence of a text, or a collection of text objects that convey an idea or summarizes a topic. Each document is allocated a single vector within the data matrix.
Within the data matrix, a weighted value is applied to each cell. The resulting weighted cells are a proportional representation of the importance of the cell's original information, for example, rare words are weighed more heavily.
Singular Value Decomposition (SVD) is applied to the data matrix to decompose the data matrix into three trained matrices. As previously discussed, the first trained matrix consists of the original row (text object) identities, and columns of orthogonal derived factor values. The second trained matrix consists of the original column (segment) identities, and rows of orthogonal derived factor values. The third trained matrix consists of a diagonal matrix containing scaling values.
The number of dimensions in these trained matrices are further reduced by setting the smallest values in the scaling matrix to zero. The reduced dimensions describe major associational structures in the data matrix, but ignore small variations in the word usage. The optimal dimensionality for the final solution is determined empirically for each application depending upon the desired results. Typically more than 100 dimensions are used.
Applying matrix multiplication to these three matrices creates the semantic-space matrix, Y
The student's essay, otherwise known as the ungraded sample text, is read and stored by element
This pseudo-object can be computed using either of the following two methods. Both methods are mathematically equivalent. In a first method, each vector element is determined by locating each parsed text object within the semantic-space matrix. Each cell value for the text object, that is vector element, is summed over the entire row in the semantic-space matrix. This is repeated for each text object found within the ungraded sample text. The vector representation is the average, or alternatively the sum, of the vector elements the ungraded sample text contains.
In another method, the three trained matrices are used to compute the pseudo-object for the ungraded sample text. In this case, first define a pseudo-document vector using the parsed text objects as vector elements. Matrix multiply the transposed pseudo-document vector to the first trained matrix and to the inverse of the third trained matrix, that is, equation 4. Next, sum or average each element of the segment vector. This pseudo-document vector can be used as a segment row representing any document within the second trained matrix. A person skilled in the art will note, both methods to compute the pseudo-object are mathematically equivalent.
In the same manner as discussed previously, element
The steps required to create the semantic-space matrix are similar to previous embodiments. Element
The data matrix does not have to be created with the standard reference text. If the standard reference text is used to create the data matrix, element
Next the degree of similarity is computed. Element
The process in this embodiment used to create the trained matrices is similar to previous embodiments. Element
The process used to create the pseudo-object vector representation is similar to previous embodiments. Element
This process employs the same steps used in the previous embodiments to create the trained matrices. Element
This embodiment uses the same process as used in the third embodiment to generate a pseudo-object vector representation. Element
While the invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description is to be considered as exemplary and not restrictive in character, it being understood that only the preferred embodiment and minor variants thereof have been shown and described and that all changes and modifications that come within the spirit of the invention are desired to be protected.