Title of Invention

SYSTEM AND METHOD FOR CLASSIFYING INPUT TEXT TO A TARGET CLASSIFICATION SYSTEM HAVING TWO OR MORE TARGET CLASSES

Abstract The invention relates to a computerized system (100) for classifying input text (126, 128) to a target classification system having two or more target classes (122.1, 124.1, 126.1, 128.1), the system comprising a processor (130); a database (110, 120, 140); a memory adapted to store instructions for execution by the processor, the instructions comprising a first set of instructions (131, 132, 133, 134) adapted to determine for each of the target classes at least first and second scores based on the input text and the target class; a second set of instructions (135) adapted to determine for each of the target classes a corresponding composite score based on the first score scaled by a first class-specific weight for the target class and the second score scaled by a second class specific weight for the target class; and a third set of instructions (136, 137) adapted to determine for each of the target classes whether to classify or recommend classification of the input text to the target class based on the corresponding composite score and a class-specific decision threshold for the target class. A computer-implemented method of classifying input text to a target classification system having two or more target classes is also disclosed.
Full Text Technical Field
The present invention concerns systems, methods, and software for
classifying text and documents, such as headnotes of judicial opinions.
Background
The American legal system, as well as some other legal systems around
the world, relies heavily on written judicial opinions —the written
pronouncements of judges— to articulate or interpret the laws governing
resolution of disputes, Each judicial opinion is not only important to resolving a
particular legal dispute, but also to resolving similar disputes in the future.
Because of this, judges and lawyers within our legal system are continually
researching an ever-expanding body of past opinions, or case law, for the ones
most relevant to resolution of new disputes.
To facilitate these searches, companies, such as West Publishing
Company of St. Paul, Minnesota (doing business as West Group), not only
collect and publish the judicial opinions of courts across the United States, but
also summarize and classify the opinions based on the principles or points of law
they contain. West Group, for example, creates and classifies headnotes —short


summaries of points made injudicial opinions- using its proprietary West Key
Number™ System. (West Key Number is a trademark of West Group.)
The West Key Number System is a hierarchical classification of over 20
million headnotes across more than 90,000 distinctive legal categories, or
classes. Each class has not only a descriptive name, but also a unique
alpha-numeric code, known as its Key Number classification.
In addition to highly-detailed classification systems, such as the West
Key Number System, judges and lawyers conduct research using products, such
as American Law Reports (ALR), that provide in-depth scholarly analysis of a
broad spectrum of legal issues. In fact, the ALR includes about 14,000 distinct
articles, known as annotations, each teaching about a separate legal issue, such
as double jeopardy and free speech. Each annotations also include citations
and/or headnotes identifying relevant judicial opinions to facilitate further legal
research.
To ensure their currency as legal-research tools, the ALR annotations are
continually updated to cite recent judicial opinions (or cases). However,
updating is a costly task given that courts across the country collectively issue
hundreds of new opinions every day and that the conventional technique for
identifying which of these cases are good candidates for citation is inefficient
and inaccurate.
In particular, the conventional technique entails selecting cases that have
headnotes in certain classes of the West Key Number System as candidates for
citations in corresponding annotations. The candidate cases are then sent to
professional editors for manual review and final determination of which should
be cited to the corresponding annotations. Unfortunately, this simplistic
mapping of classes to annotations not only sends many irrelevant cases to the
editors, but also fails to send many that are relevant, both increasing the
workload of the editors and limiting accuracy of the updated annotations.


Accordingly, there is a need for tools that facilitate classification or
assignment of judicial opinions to ALR annotations and other legal research
tools.
Summary of Exemplary Embodiments
To address this and other needs, the present inventors devised systems,
methods, and software that facilitate classification of text or documents
according to a target classification system. For instance, one exemplary system
aids in classifying headnotes to the ALR annotations; another aids in classifying
headnotes to sections of American Jurisprudence (another encyclopedic style
legal reference); and yet another aids in classifying headnotes to the West Key
Number System. However, these and other embodiments are applicable to
classification of other types of documents, such as emails.
More particularly, some of the exemplary systems classify or aid manual
classification of an input text by determining a set of composite scores, with
each composite score corresponding to a respective target class in the target
classification system. Determining each composite score entails computing and
and applying class-specific weights to at least two of the following types of
scores:
• a first type based on similarity of the input text to text associated with
a respective one of the target classes;
• a second type based on similarity of a set of non-target classes
associated with the input text and a set of non-target classes
associated with a respective one of the target classes;
• a third type based on probability of one of the target classes given a
set of one or more non-target classes associated with the input text;
and
• a fourth type based on a probability of the input text given text
associated with a respective one of the target classes.

-3-

These exemplary systems then evaluate the composite scores using class-specific
decision criteria, such as thresholds, to ultimately assign or recommend
assignment of the input text (or a document or other data structure associated
with the input text) to one or more of the target classes.
Brief Description of Drawings
Figure 1 is a diagram of an exemplary classification system 100
embodying teachings of the invention, including a unique
graphical user interface 114;
Figure 2 is a flowchart illustrating an exemplary method embodied in
classification system 100 of Figure 1;
Figure 3 is a diagram of an exemplary headnote 310 and a corresponding
noun-word-pair model 320.
Figure 4 is a facsimile of an exemplary graphical user interface 400 that
forms a portion of classification system 100.
Figure 5 is a diagram of another exemplary classification system 500,
which is similar to system 100 but includes additional classifiers;
and
Figure 6 is a diagram of another exemplary classification system 600,
which is similar to system 100 but omits some classifiers.
Detailed Description of Exemplary Embodiments
This description, which references and incorporates the above-identified
Figures, describes one or more specific embodiments of one or more inventions.
These embodiments, offered not to limit but only to exemplify and teach the one
or more inventions, are shown and described in sufficient detail to enable those
skilled in the art to implement or practice the invention. Thus, where
appropriate to avoid obscuring the invention, the description may omit certain
information known to those of skill in the art.


The description includes many terras with meanings derived from their
usage in the art or from their use within the context of the description. However,
as a further aid, the following exemplary definitions are presented.
The term "document" refers to any addressable collection or
arrangement of machine-readable data.
The term "database" includes any logical collection or
arrangement of documents.
The term "headnote" refers to an electronic textual summary or
abstract concerning a point of law within a written judicial opinion. The
number of headnotes associated with a judicial opinion (or case) depends
on the number of issues it addresses.
Exemplary System for Classifying Headnotes to American Legal Reports
Figure 1 shows a diagram of an exemplary document classification
system 100 for automatically classifying or recommending classifications of
electronic documents according to a document classification scheme. The
exemplary embodiment classifies or recommends classification of cases, case
citations, or associated headnotes, to one or more of the categories represented
by 13,779 ALR annotations. (The total number of annotation is growing at a
rate on the order of 20-30 annotations per month.) However, the present
invention is not limited to any particular type of documents or type of
classification system.
Though the exemplary embodiment is presented as an interconnected
ensemble of separate components, some other embodiments implement their
functionality using a greater or lesser number of components. Moreover, some
embodiments intercouple one or more the components through a local- or wide-
area network. (Some embodiments implement one or more portions of system
100 using one or more mainframe computers or servers.) Thus, the present
invention is not limited to any particular functional partition.
System 100 includes an ALR annotation database 110, a headnotes


database 120, and a classification processor 130, a preliminary classification
database 140, and editorial workstations 150.
ALR annotation database 110 (more generally a database of electronic
documents classified according to a target classification scheme) includes a set
of 13,779 annotations, which are presented generally by annotation 112. The
exemplary embodiment regards each annotation as a class or category. Each
annotation, such as annotation 112, includes a set of one or more case citations,
such as citations 112.1 and 112.2.
Each citation identifies or is associated with at least one judicial opinion
(or generally an electronic document), such as electronic judicial opinion (or
case) 115. Judicial opinion 115 includes and/or is associated with one or more
headnotes in headnote database 120, such as headnotes 122 and 124. (In the
exemplary embodiment, a typical judicial opinion or case has about 6 associated
headnotes, although cases having 50 or more are not rare.)
A sample headnote and its assigned West Key Number class identifier are shown
below.
Exemplary Headnote;
In an action brought under Administrative Procedure Act (APA), inquiry is twofold: court first examines the
organic statute to determine whether Congress intended that an aggrieved party follow a particular
administrative route before judicial relief would become available; if that generative statute is silent, court
then asks whether an agency's regulations require recourse to a superior agency authority.
Exemplary Key Number class identifier.-
15AK229 - ADMINISTRATIVE LAW AND PROCEDURE - SEPARATION OF ADMINISTRATIVE
AND OTHER POWERS-JUDICIAL POWERS
In database 120, each headnote is associated with one or more class
identifiers, which are based, for example, on the West Key Number
Classification System. (For fiirther details on the West Key Number System, see
West's Analysis of American Law: Guide to the American Digest System, 2000
Edition, West Group, 1999, which is incorporated herein by reference.) For
example, headnote 122 is associated with classes or class identifiers 122.1,


122.2, and 122.3, and headaotc 124 is associated with classes or class identifiers
124.1 and 124.2.
In the exemplary system, headnote database 120 includes about 20
million headnotes and grows at an approximate rate of 12,000 headnotes per
week. About 89% of the headnotes are associated with a single class identifier,
about 10% with two class identifiers, and about 1% with more than two class
identifiers.
Additionally, headnote database 120 includes a number of headnotes,
such as headnotes 126 and 128, that are not yet assigned or associated with an
ALR annotation in database 110. The headnotes, however, are associated with
class identifiers. Specifically, headnote 126 is associated with class identifiers
126.1 and 126.2, and headnote 128 is associated with class identifier 128.1.
Coupled to both ALR annotation database 110 and headnote database
120 is classification processor 130. Classification processor 130 includes
classifiers 131,132,133, and 134, a composite-score generator 135, an
assignment decision-maker 136, and decision-criteria module 137. Processor
130 determines whether one or more cases associated with headnotes in
headnote database 120 should be assigned to or cited within one or more of the
annotations of annotation database 110. Processor 130 is also coupled to
preliminary classification database 140.
Preliminary classification database 140 stores and/or organizes the
assignment or citation recommendations. Within database 140, the
recommendations can be organized as a single first-in-first-out (FIFO) queue, as
multiple FIFO queues based on single annotations or subsets of annotations.
The recommendations are ultimately distributed to work center 150.
Work center 150 communicates with preliminary classification database
140 as well as annotation database 110 and ultimately assists users in manually
updating the ALR annotations in database 110 based on the recommendations
stored in database 140. Specifically, work center 150 includes workstations 152,
154, and 156. Workstation 152, which is substantially identical to workstations


154 and 156, includes a graphical-user interface 152.1, and user-interface
devices, such as a keyboard and mouse (not shown.)
In general, exemplary system 100 operates as follows. Headnotes
database 120 receives a new set of headnotes (such as headnotes 126 and 128)
for recently decided cases, and classification processor 130 determines whether
one or more of the cases associated with the headnotes are sufficiently relevant
to any of the annotations within ALR to justify recommending assignments of
the headnotes (or associated cases) to one or more of the annotations. (Some
other embodiments directly assign the headnotes or associated cases to the
annotations.) The assignment recommendations are stored in preliminary
classification database 140 and later retrieved by or presented to editors in work
center 150 via graphical-user interfaces in workstations 152,154, and 156 for
acceptance or rejection. Accepted recommendations are added as citations to the
respective annotations in ALR annotation database 110 and rejected
recommendations are not. However, both accepted and rejected
recommendations are fed back to classification processor 130 for incremental
training or tuning of its decision criteria.
More particularly, Figure 2 shows a flow chart 200 illustrating in greater
detail an exemplary method of operating system 100. Flow chart 200 includes a
number of process blocks 210-250. Though arranged serially in the exemplary
embodiment, other embodiments may reorder the blocks, omits one or more
blocks, and/or execute two or more blocks in parallel using multiple processors
or a single processor organized as two or more virtual machines or
subprocessors. Moreover, still other embodiments implement the blocks as one
or more specific interconnected hardware or integrated-circuit modules with
related control and data signals communicated between and through the
modules. Thus, the exemplary process flow is applicable to software, firmware,
hardware, and hybrid implementations.
The remainder of the description uses the following notational system.
The lower case letters a, h, and k respectively denote an annotation, a headnote,


and a class or class identifier, such as a West Key Number class or class
identifier. The upper case letters A, H, and K respectively denote the set of all
annotations, the set of all headnotes, and the set of all key numbers
classifications. Additionally, variables denoting vector quantities are in bold-
faced capital letters, and elements of the corresponding vectors are denoted in
lower case letters. For example.V denotes a vector, and v denotes an element of
vector V.
At block 210, the exemplary method begins by representing the
annotations in annotations database 110 (in Figure 1) as text-based feature
vectors. In particular, this entails representing each annotation a as a one-
column feature vector, Va, based on the noun and/or noun-word pairs occurring
in headnotes for the cases cited within the annotation. (Other embodiments
represent the headnotes as bigrams or noun phrases.)
Although it is possible to use all the headnotes associated with the cases
cited in the annotation, the exemplary embodiment selects from the set of all
headnotes associated with the cited cases those that are most relevant to the
annotation being represented. For each annotation, this entails building a feature
vector using all the headnotes in all cases cited in the annotation and selecting
from each case one, two, or three headnotes based on similarity between the
headnotes in a cited case and those of the citing annotation and denoting the
most similar headnote(s) as relevant. To determine the most relevant headnotes,
the exemplary embodiment uses classifiers 131-134 to compute similarity
scores, averages the four scores for each headnote, and defines as most relevant
the highest scoring headnote plus those with a score of at least 80% of the
highest score. The 80% value was chosen empirically.
Once selected, the associated headnotes (or alternatively the actual text of
the annotations) are represented as a set of nouns, noun-noun, noun-verb, and
noun-adjective pairs that it contains. Words in a word-pair are not necessarily
adjacent, but are within a specific number of words or characters of each other,
that is, within a particular word or character window. The window size is


adjustable and can take values from 1 to the total number of words or characters
in the headnote. Although larger windows tend to yield better performance, in
the exemplary embodiment, no change in performance was observed for
windows larger than 32 non-stop words. For convenience, however, the
exemplary window size is set to the actual headnote size. The exemplary
embodiment excludes stop words and uses the root form of all words. Appendix
A shows an exemplary list of exemplary stopwords; however, other
embodiments use other lists of stopwords.
Figure 3 shows an example of a headnote 310 and a noun-word
representation 320 in accord with the exemplary embodiment. Also shown are
West Key Number classification text 330 and class identifier 340.
In a particular annotation vector Va, the weight, or magnitude, of any
particular element va is defined as

where tf'a denotes the term frequency (that is, the total number of occurrences) of
the term or noun-word pair associated with annotation a. (In the exemplary
embodiment, this is the number of occurrences of the term within the set of
headnotes associated with the annotation.) idf'a denotes the inverse document
frequency for the associated term or noun-word pair. idf'a is defined as
i
where N is the total number of headnotes (for example, 20 million) in the
collection, and df'a is the number of headnotes (or more generally documents)
containing the term or noun-word pair. The prime ' notation indicates that these
frequency parameters are based on proxy text, for example, the text of associated
headnotes, as opposed to text of the annotation itself. (However, other
embodiments may use all or portions of text from the annotation alone or in
combination with proxy text, such as headnotes or other related documents.)

Even though the exemplary embodiment uses headnotes associated with
an annotation as opposed to text of the annotation itself, the annotation-text
vectors can include a large number of elements. Indeed, some annotation
vectors can include hundreds of thousands of terms or noun-word pairs, with the
majority of them having a low term frequency. Thus, not only to reduce the
number of terms to a manageable number, but also to avoid the rare-word
problem known to exist in vector-space models, the exemplary embodiment
removes low-weight terms.
Specifically, the exemplary embodiment removes as many low-weight
terms as necessary to achieve a lower absolute bound of 500 terms or a 75%
reduction in the length of each annotation vector. The effect of this process on
the number of terms in an annotation vector depends on their weight distribution.
For example, if the terms have similar weights, approximately 75% of the terms
will be removed. However, for annotations with skewed weight distributions, as
few as 10% of the terms might be removed. In the exemplary embodiment, this
process decreased the total number of unique terms for all annotation vectors
from approximately 70 million to approximately 8 million terms.
Some other embodiments use other methods to limit vector size. For
example, some embodiments apply a fixed threshold on the number of terms per
category, or on the term's frequency, document frequency, or weight These
methods are generally efficient when the underlying categories do not vary
significantly in the feature space. Still other embodiments perform feature
selection based on measures, such as mutual information. These methods,
however, are computationally expensive. The exemplary method attempts to
strike a balance between these two ends.
Block 220, executed after representation of the annotations as text-based
feature vectors, entails modeling one or more input headnotes from database 120
(in Figure 1) as a set of corresponding headnote-text vectors. The input
headnotes include headnotes that have been recently added to headnote database

120 or that have otherwise not previously been reviewed for relevance to the
ALR annotations in database 110.
The exemplary embodiment represents each input headnote h as a
vector Vk, with each element vk, like the elements of the annotation vectors,
associated with a term or noun-word pair in the headnote. vk is defined as

where tfh denotes the frequency (that is, the total number of occurrences) of the
associated term or noun-word pair in the input headnote, and idfH denotes the
inverse document frequency of the associated term or noun-word pair within all
the headnotes.
At block 230, the exemplary method continues with operation of
classification processor 130 (in Figure 1). Figure 2 shows that block 230 itself
comprises sub-process blocks 231-237.
Block 231, which represents operation of classifier 131, entails
computing a set of similarity scores based on the similarity of text in each input
headnote text to the text associated with each annotation. Specifically, the
exemplary embodiment measures this similarity as the cosine of the angle
between the headnote vector Vk and each annotation vector Va.
Mathematically, this is expressed as

where "." denotes the conventional dot- or inner-product operator, and Va and
Vh denote that respective vectors Va and Vh have been modified to include
elements corresponding to terms or noun-word pairs found in both the
annotation text and the headnote. In other words, the dot product is computed
based on the intersection of the terms or noun-word pairs. denotes the

length of the vector argument. In this embodiment, the magnitudes are
computed based on all the elements of the vector.
Block 232, which represents operation of classifier 132, entails determining a
set of similarity scores based on the similarity of the class identifiers (or other
meta-data) associated with the input headnote and those associated with each of
the annotations. Before this determination is made, each annotation a is
represented as an annotation-class vector VaC vector, with each element
vac indicating the weight of a class identifier assigned to the headnotes cited by
the annotation. Each element vac is defined as

where tfac denotes the frequency of the associated class identifier, and idfac,
denotes its inverse document frequency, idfac is defined as

where Nc is the total number of classes or class identifiers. In the exemplary
embodiment, Nc is 91997, the total number of classes in the West Key Number
System. dfc is the frequency of the class identifier amongst the set of class
identifiers for annotation a. Unlike the exemplary annotation-text vectors winch
are based on a selected set of annotation headnotes, the annotation-class vectors
use all the class identifiers associated with all the headnotes that are associated
with the annotation. Some embodiments may use class-identifier pairs, although
they were found to be counterproductive in the exemplary implementation.
Similarly, each input headnote is also represented as a headnote-class
vector with each element indicating the weight of a class or class identifier
assigned to the headnote. Each element is defined as


with tfhc denoting the frequency of the class identifier, and idfhc denoting the
inverse document frequency of the class identifier. idfhc is defined as

where Nc is the total number of classes or class identifiers and dfh is the
frequency of the class or class identifier amongst the set of class or class
identifiers associated with the annotation.
Once the annotation-class and headnote-class vectors are established,
classification processor 130 computes each similarity score S2 as the cosine of
the angle between them. This is expressed as

For headnotes that have more than one associated class identifier, the exemplary
embodiment considers each class identifier separately of the others for that
headnote, ultimately using the one yielding the maximum class-identifier
similarity. The maximization criteria is used because, in some instances, a
headnote may have two or more associated class identifiers (or Key Number
classifications), indicating its discussion of two or more legal points. However,
in most cases, only one of the class identifiers is relevant to a given annotation,
In block 233, classifier 133 determines a set of similarity scores S3 based
on the probability that a headnote is associated with a given annotation from
class-identifier (or other meta-data) statistics. This probability is approximated
by

where {k}h denotes the set of class identifiers assigned to headnote h. Each
annotation conditional class probability P(k/a)is estimated by


where tf(k,a) is the terra frequency of the k-th class identifier among the class
identifiers associated with the headnotes of annotation a; |a| denotes the total
number of unique class identifiers associated with annotation a (that is, the
number of samples or cardinality of the set); and denotes the sum of
the term frequencies for all the class identifiers.
The exemplary determination of similarity scores S3 relies on
assumptions that class identifiers are assigned to a headnote independently of
each other, and that only one class identifier in {k}h is actually relevant to
annotation a. Although the one-class assumption does not hold for many
annotations, it improves the overall performance of the system.
Alternatively, one can multiply the conditional class-identifier (Key
Number classifications) probabilities for the annotation, but this effectively
penalizes headnotes with multiple Key Number classifications (class
assignments), compared to those with single Key Number classifications. Some
other embodiments use Bayes' rule to incorporate a priori probabilities into
classifier 133. However, some experimentation with this approach suggests that
system performance is likely to be inferior to that provided in this exemplary
implementation.
The inferiority may stem from the fact that annotations are created at
different times, and the fact that one annotation has more citations than another
does not necessarily mean it is more probable to occur for a given headnote.
Indeed, a greater number of citations might only reflect that one annotation has
been in existence longer and/or updated more often than another. Thus, other
embodiments might use the prior probabilities based on the frequency that class
numbers are assigned to the annotations.

In block 234, classifier 134 determines a set of similarity scores S4, based
on . the probability of each annotation given the text of the input
headnote. In deriving a practical expression for computing . the
exemplary embodiment first assumes that an input headnote h is completely
represented by a set of descriptors T, with each descriptor t assigned to a
headnote with some probability, . Then, based on the theory of total
probability and Bayes' theorem, 1 is expressed as

Assuming that a descriptor is independent of the class identifiers associated with
a headnote allows one to make the approximation:

and to compute the similarity scores S4 according to

where. is approximated by

denotes the frequency of term t in the headnote and denotes the
sum of the frequencies of all terms in the headnote. is defined according
to Bayes1 theorem as

where P(a) denotes the prior probability for annotation a, and , the
probability of a discriminator / given annotation a, is estimated as


and denotes summation over all annotations a 'in the set of annotations .A.
Since all the annotation prior probabilities P(a) and P(a') are assumed to be
equal, is computed using

Block 235, which represents operation of composite-score generator 135,
entails computing a set of composite similarity scores CSah based on the sets of
similarity scores determined at blocks 231-235 by classifiers 131-135, with each
composite score indicating the similarity of the input headnote h to each
annotation a. More particularly, generator 135 computes each composite
score CSah according to

where denotes the similarity score of the i-th similarity score generator for
the input headnote h and annotation a, and wia is a weight assigned to the i -th
similarity score generator and annotation a. Execution of the exemplary
method then continues at block 236.
At block 236, assignment decision-maker 136 recommends that the input
headnote or a document, such as a case, associated with the headnote be
classified or incorporated into one or more of the annotations based on the set of
composite scores and decision criteria within decision-criteria module 137. In
the exemplary embodiments, the headnote is assigned to annotations according
to the following decision rule:
, then recommend assignment of h orDh to annotation a, (20)

where is an annotation-specific threshold from decision-criteria module 137
and Dh denotes a document, such as a legal opinion, associated with the
headnote. (In the exemplary embodiment, each ALR annotation includes the
text of associated headnotes and its full case citation.)
The annotation-classifier weights wia, for i = 1 to 4, and the
annotation thresholds are learned during a tuning phase. The
weights, , reflect system confidence in the ability of each similarity
score to route to annotation a. Similarly, the annotation thresholds
are also learned and reflect the homogeneity of an annotation. In general,
annotations dealing with narrow topics tend to have higher thresholds than those
dealing with multiple related topics.
In this ALR embodiment, the thresholds reflect that, over 90% of the
headnotes (or associated documents) are not assigned to any annotations.
Specifically, the exemplary embodiment estimates optimal annotation-classifier
weights and annotation thresholds through exhaustive search over a five-
dimensional space. The space is discretized to make the search manageable. The
optimal weights are those corresponding to maximum precision at recall levels
of at least 90%.
More precisely, this entails trying every combination of four weight
variables, and for each combination, trying 20 possible threshold values over the
interval [0,1]. The combination of weights and threshold that yields the best
precision and recall is then selected. The exemplary embodiment excludes any
weight-threshold combinations resulting in less than 90% recall.
To achieve higher precision levels, the exemplary embodiment
effectively requires assignments to compete for their assigned annotations or
target classifications. This competition entails use of the following rule:
Assign h to a, iff
where a denotes an empirically determined value greater than zero and less
than 1, for example, 0.8; denotes the maximum composite similarity score

associated with a headnote in the set of headnotes assigned to annotation
a.
Block 240 entails processing classification recommendations from
classification processor 130. To this end, processor 130 transfers classification

Button 440, labeled "New Section," allows a user to create a new section
or subsection in the annotation outline. This feature is useful, since in some
instances, a headnote suggestion is good, but does not fit an existing section of
the annotation. Creating the new section or subsection thus allows for convenient
expansion of the annotation..
Button 450 toggles on and off the display of a text box describing
headnote assignments made to the current annotation during the current session,
In the exemplary embodiment, the text box presents each assignment in a short
textual form, such as identifier > This feature is particularly convenient for
larger annotation outlines that exceed the size of window 430 and require
scrolling contents of the window.
Button 460, labeled "Un-Allocate," allows a user to de-assign, or
declassify, a headnote to a particular annotation. Thus, if a user changes her
mind regarding a previous, unsaved, classification, the user can nullify the
classification. In some embodiments, headnotes identified in window 410 are
understood to be assigned to the particular annotation section displayed in
window 430 unless the user decides that the assignment is incorrect or
inappropriate. (In some embodiments, acceptance of a recommendation entails
automatic creation of hyperlinks linking the annotation to the case and the case
to the annotation.)
Button 470, labeled "Next Annotation," allows a user to cause display of
the set of headnotes recommended for assignment to the next annotation.
Specifically, this entails not only retrieving headnotes from preliminary
classification storage 140 and displaying them in window 410, but also
displaying the relevant annotation outline within window 430.
Button 480, labeled "Skip Anno," allows a user to skip the current
annotation and its suggestions altogether and advance to the next set of
suggestions and associated annotation. This feature is particularly useful when
an editor wants another editor to review assignments to a particular annotation,

or if the editor wants to review this annotation at another time, for example, after
reading or studying the entire annotation text, for example. The suggestions
remain in preliminary classification database 140 until they are either reviewed
or removed. (In some embodiments, the suggestions are time-stamped and may
be supplanted with more current suggestions or deleted automatically after a
preset period of time, with the time period, in some variations dependent on the
particular annotation.)
Button 490, labeled "Exit," allows an editor to terrninate an editorial
session. Upon termination, acceptances and recommendations are stored in ALR
annotations database 110.
Figure 2 shows that after processing of the preliminary classifications,
execution of the exemplary method continues at block 250. Block 250 entails
updating of classification decision criteria. In the exemplary embodiment, this
entails counting the numbers of accepted and rejected classification
recommendations for each annotation, and adjusting the annotation-specific .
decision thresholds and/or classifier weights appropriately. For example, if 80%
of the classification recommendations for a given annotation are rejected during
one day, week, month, quarter or year, the exemplary embodiment may increase
the decision threshold associated with that annotation to reduce the number of
recommendations. Conversely, if 80% are accepted, the threshold may be
lowered to ensure that a sufficient number of recommendations are being
considered.
Exemplary System for Classifying Headnotes to American Jurisprudence
Figure 5 shows a variation of system 100 in the form of an exemplary
classification system 500 tailored to facilitate classification of documents to one
or more of the 135,000 sections of The American Jurisprudence (AmJur).
Similar to an ALR annotation, each AmJur section cites relevant cases as they
are decided by the courts. Likewise, updating AmJur is time consuming.


In comparison to system 100, classification system 500 includes six
classifiers; classifiers 131-134 and classifiers 510 and 520, a composite score
generator 530, and assignment decision-maker 540. Classifiers 131-134 are
identical to the ones used in system 100, with the exception that they operate on
AmJur data as opposed to ALR data.
Classifiers 510 and 520 process AmJur section text itself, instead of
proxy text based on headnotes cited within the AmJur section.. More
specifically, classifier 510 operates using the formulae underlying classifier 131
to generate similarity measurements based on the tf-idfs (term-frequency-inverse
document frequency) of noun-word pairs in AmJur section text. And classifier
520 operates using the formulae underlying classifier 134 to generate similarity
measurements based on the probabilities of a section text given the input
headnote.
Once the measurements are computed, each classifier assigns each
AmJur section a similarity score based on a numerical ranking of its respective
set of similarity measurements. Thus, for any input headnote, each of the six
classifiers effectively ranks the 135,000 AmJur sections according to their
similarities to the headnote. Given the differences in the classifiers and the data
underlying their scores, it is unlikely that all six classifiers would rank the most
relevant AmJur section the highest; differences in the classifiers and the data
they use generally suggest that this will not occur. Table 1 shows a partial
ranked listing of AmJur sections showing how each classifier scored, or ranlced,
their similarity to a given headnote.



Composite score generator 530 generates a composite similarity score for
each AmJur section based on its corresponding set of six similarity scores. In
the exemplary embodiment, this entails computing the median of the six scores
for each AmJur section. However, other embodiments can compute a uniform
or non-uniformly weighted average of all six or a subset of the six rankings.
Still other embodiments can select the maximum, minimum, or mode as the
composite score for the AmJur section. After generating the composite scores,
the composite score generator forwards data identifying the AmJur section
associated with the highest composite score, the highest composite score, and the
input headnote to assignment decision-maker 540.
Assignment decision-maker 540 provides a fixed portion of headnote-
classification recommendations to preliminary classification database 140, based
on the total number of input headnotes per a fixed time period. The fixed

number and time period governing the number of recommendations are
determined according to parameters within decision-criteria module 137. For
example, one embodiment ranks all incoming headnotes for the time period,
based on their composite scores and recommends only those headnotes that rank
in the top 16 percent.
In some instances, more than one headnote may have a composite score
that equals a given cut-off threshold, such as top 16%. To ensure greater
accuracy in these circumstances, the exemplary embodiment re-orders all
headnote-section pairs that coincide with the cut-off threshold, using the six
actual classifier scores.
This entails converting the six classifier scores for a particular headnote-
section pair into six Z-scores and then multiplying the six Z-scores for a
particular headnote-section pair to produce a single similarity measure. (Z-scores
are obtained by assuming that each classifier score has a normal distribution,
estimating the mean and standard deviation of the distribution, and then
subtracting the mean from the classifier score and dividing the result by the
standard deviation.) The headnote-section pairs that meet the acceptance criteria
are than re-ordered, or re-ranked, according to this new similarity measure, with
as many as needed to achieve the desired number of total recommendations
being forwarded to preliminary classification database 140. (Other embodiments
may apply this "reordering" to all of the headnote-section pairs and then filter
these based on the acceptance criteria necessary to obtain the desired number of
recommendations.)
Exemplary System for Classifying Headnotes to West Key Number System
Figure 6 shows another variation of system 100 in the form of an
exemplary classification system 600 tailored to facilitate classification of input
headnotes to classes of the West Key Number System. The Key Number System
is a hierarchical classification system with 450 top-level classes, which are
further subdivided into 92,000 sub-classes, each having a unique class identifier.


In comparison to system 100, system 600 includes classifiers 131 and 134, a
composite score generator 610, and an assignment decision-maker 620.
In accord with previous embodiments, classifiers 131 and 134 model
each input headnote as a feature vector of noun-word pairs and each class
identifier as a feature vector of noun-word pairs extracted from headnotes
assigned to it. Classifier 131 generates similarity scores based on the tf-idf
products for noun-word pairs in headnotes assigned to each class identifier and
to a given input headnote. And classifier 134 generates similarity scores based
on the probabilities of a class identifier given the input headnote. Thus, system
600 generates over 184,000 similarity scores, with each scores representing the
similarity of the input headnote to a respective one of the over 92,000 class
identifiers in the West Key Number System using a respective one of the two
classifiers.
Composite score generator 610 combines the two similarity measures for
each possible headnote-class-identifier pair to generate a respective composite
similarity score. In the exemplary embodiment, this entails defining, for each
class or class identifier, two normalized cumulative histograms (one for each
classifier) based on the headnotes already assigned to the class. These
histograms approximate corresponding cumulative density functions, allowing
one to determine the probability that a given percentage of the class identifiers
scored below a certain similarity score.
More particularly, the two cumulative normalized histograms for class-
identifier c, based on classifiers 131 and 134 are respectively denoted FC1 and
Fc2, and estimated according to:


where c denotes a particular class or class identifier;
5 = 0, 0.01, 0.02, 0.03,-, 1.0; F(s headnotes classified to or associated with class or class identifier denotes
the number of elements in the set denotes the set of headnotes
already classified or associated with class or class identifier denotes the
similarity score for headnote hi and class-identifier c, as measured by classifier
131, and denote the similarity score for headnote and class-identifier c, as
measured by classifier 134. (In this context, each similarity score indicates the
similarity of a given assigned headnote to all the headnotes assigned to class c.)
In other words, denotes the number of headnotes assigned to class c
that received a score of s from classifier 131, and denotes the
number of headnotes assigned to class c that received a score of s from classifier
134.
Thus, for every possible score value (between 0 and 1 with a particular
score spacing), each histogram provides the percentage of assigned headnotes
that scored higher and lower than that particular score. For example, for
classifier 131, the histogram for class identifier c might show that 60% of the set
of headnotes assigned to classifier c scored higher than 0.7 when compared to
the set of headnotes as a whole; whereas for classifier 134 the histogram might
show that 50% of the assigned headnotes scored higher than 0.7
Next, composite score generator 610 converts each score for the input
headnote into a normalized similarity score using the corresponding histogram
and computes each composite score for each class based on the normalized
scores. In the exemplary embodiment, this conversion entails mapping each
classifier score to the corresponding histogram to determine its cumulative
probability and then multiplying the cumulative probabilities of respective pairs
of scores associated with a given class c to compute the respective composite

similarity score. The set of composite scores for the input headnote are then
processed by assignment decisionmaker 620.
Assignment decision maker 620 forwards a fixed number of the top
scoring class identifiers to preliminary classification database 140. The
exemplary embodiments suggest the class identifiers having the top five
composite similarity scores for every input headnote.
Other Exemplary Applications
The components of the various exemplary systems presented can he
combined in myriad ways to form other classification systems of both greater
and lesser complexity. Additionally, the components and systems can be
tailored for other types of documents other than headnotes. Indeed, the
components and systems and embodied teachings and principles of operation are
relevant to virtually any text or data classification context.
For example, one can apply one or more of the exemplary systems and
related variations to classify electronic voice and mail messages. Some mail
classifying systems may include one or more classifiers in combination with
conventional rules which classify messages as useful or SPAM based on whether
the sender is in your address book, same domain as recipient, etc.

Appendix A
Exemplary Stop Words
a a.m ab about above accordingly across ad after afterward afterwards again
against ago ah ahead ain't all allows almost alone along already alright also
although always am among amongst an and and/or anew another ante any
anybody anybody's anyhow anymore anyone anyone's anything anything's
anytime anytime's anyway anyways anywhere anywhere's anywise appear
approx are aren't around as aside associated at available away awfully awhile b
banc be became because become becomes becoming been before beforehand
behalf behind being below beside besides best better between beyond both brief

but by bythe c came can can't cannot cant cause causes certain certainly cetera cf
ch change changes cit cl clearly cmt co concerning consequently consider
contain containing contains contra corresponding could couldn't course curiam
currently d day days dba de des described di did didn't different divers do does
doesn't doing don't done down downward downwards dr du during e e.g each ed
eds eg eight eighteen eighty either eleven else elsewhere enough especially et etc
even ever evermore every everybody everybody's everyone everyone's
everyplace everything everything's everywhere everywhere's example except f
facie facto far few fewer fide fides followed following follows for forma former
formerly forth forthwith fortiori fro from further furthermore g get gets getting
given gives go goes going gone got gotten h had hadn't happens hardly has hasn't
have haven't having he he'd he'll he's hello hence henceforth her here here's
hereabout hereabouts hereafter herebefore hereby herein hereinafter hereinbefore
hereinbelow hereof hereto heretofore hereunder hereunto hereupon herewith hers
herself hey hi him himself his hither hitherto hoc hon how howbeit however
howsoever hundred i i'd i'll i'm i've i.e ibid ibidem id ie if ignored ii iii illus
immediate in inasmuch inc indeed indicate indicated indicates infra initio insofar
instead inthe into intra inward ipsa is isn't it it's its itself iv ix j jr judicata just k
keep kept kinda know known knows 1 la last later latter latterly le least les less
lest let let's like likewise little looks ltd m ma'am many may maybe me


meantime meanwhile mero might million more moreover most mostly motu mr
mrs ms much must my myself name namely naught near necessaiy neither never
nevermore nevertheless new next no no-one nobody nohow nolo nora non none
nonetheless noone nor normally nos not nothing novo now nowhere o o'clock of
ofa off ofhis oft often ofthe ofthis oh on once one one's ones oneself only onthe
onto op or other others otherwise ought our ours ourself ourselves out outside
over overall overly own p p.m p.s par para paras pars particular particularly
passim per peradventure percent perchance perforce perhaps pg pgs placed
please plus possible pp probably provides q quite r rata rather really rel relatively
rem res resp respectively right s sa said same says se sec seem seemed seeming
seems seen sent serious several shall shalt she she'll she's should shouldn't since
sir so some somebody somebody's somehow someone someone's something
something's sometime sometimes somewhat somewhere somewhere's specified
specify specifying still such sundry sup t take taken tarn than that that's thats the
their theirs them themselves then thence thenceforth thenceforward there there's
thereafter thereby therefor therefore therefrom therein thereof thereon theres
thereto theretofore thereunto thereupon therewith these they they'll thing things
third this thither thorough thoroughly those though three through througliout thru
thus to to-wit together too toward towards u uh unless until up upon upward
upwards used useful using usually v v.s value various very vi via vii viii
virtually vs w was wasn't way we we'd we'll we're we've well went were weren't
what what'll what's whatever whatsoever when whence whenever where
whereafter whereas whereat whereby wherefore wherefrom wherein whereinto
whereof whereon wheresoever whereto whereunder whereunto whereupon
wherever wherewith whether which whichever while whither who who'd who'll
who's whoever whole wholly wholy whom whose why will with within without
won't would wouldn't x y y'all ya'll ye yeah yes yet you you'll you're you've your
yours yourself yourselves z


Conclusion
In furtherance of the art, the inventors have presented various exemplary
systems, methods, and software which facilitate the classification of text, such as
headnotes or associated legal cases to a classification system, such as that
represented by the nearly 14,000 ALR annotations. The exemplary system
classifies or makes classification recommendations based on text and class
similarities and probabilistic relations. The system also provides a graphical-
user interface to facilitate editorial processing of recommended classifications
and thus automated update of document collections, such as the American Legal
Reports, American Jurisprudence, and countless others.
The embodiments described above are intended only to illustrate and
teach one or more ways of practicing or implementing the present invention, not
to restrict its breadth or scope. The actual scope of the invention, which
embraces all ways of practicing or implementing the teachings of the invention,
is defined only by the following claims and their equivalents.

We Claim :
1. A computerized system (100, 500, 600) for classifying input text (126, 128) to a target
classification system having two or more target classes (122.1, 124.1, 126.1, 128.1), the system
comprising:
a processor (130);
a database (110, 120, 140);
a memory adapted to store instructions for execution by the processor, the instructions
comprising:
a first set of instructions (131, 132, 133, 134) adapted to determine for each of the target
classes at least first and second scores based on the input text and the target class;
a second set of instructions (135) adapted to determine for each of the target classes a
corresponding composite score based on the first score scaled by a first class-specific weight for
the target class and the second score scaled by a second class specific weight for the target class;
and
a third set of instructions (136, 137) adapted to determine for each of the target classes
whether to classify or recommend classification of the input text to the target class based on the
corresponding composite score and a class-specific decision threshold for the target class.
2. A computer-implemented method of classifying input text to a target classification system
having two or more target classes, the method comprising:
for each target class:
providing at least first and second class-specific weights and a class specific decision
threshold;
using at least first and second classification methods to determine respective first and
second scores based on the input text and the target class;
determining a composite score based on the first score scaled by the first class-specific
weight for the class and the second score scaled by the second class-specific weight for the target
class; and
classifying or recommending classification of the input text to the target class based on
the composite score and the class-specific decision threshold.

3. The method as claimed in claim 2, wherein at least one of the first and second scores is
based on a set of one or more noun-words pairs associated with the input text and a set of one or
more noun-word pairs associated with the target class, with at least one noun-word pair (320) in
each set including a noun and a nonadjacent word.
4. The method as claimed in claim 2, wherein providing each first and second class specific
weight and class-specific decision threshold comprises searching for a combination of first and
second class-specific weights and class-specific decision threshold that yield a predetermined
level of precision at a predetermined level of recall based on text classified to the target
classification system.
5. The method as claimed in claim 2, wherein a non-target classification system includes
two or more non-target classes, and at least one of the first and second scores is based on one or
more of the non-target classes that are associated with the input text and one or more of the non-
target classes that are associated with the target class.
6. The method as claimed in claim 5,
wherein the input text is a headnote for a legal document; and
wherein the target classification system and the non-target classification system are legal
classification systems.
7. The method as claimed in claim 2, wherein the target classification system has more than
1000 target classes.
8. The method as claimed in claim 2, which involves:
displaying a graphical user interface including first and second regions, with the first
region displaying or identifying at least a portion of the input text and the second region
displaying information regarding the target classification system and at least one target class for
which the input text was recommended for classification; and
displaying a selectable feature on the graphical user interface, wherein selecting the
feature initiates classification of the input text to the one target class.
9. The method as claimed in claim 2, which involves:
for each target class:
determining first and second scores based on the input text and the target class;

determining a composite score based on the first score scaled by a first class-specific
weight for the target class and the second score scaled by a second class-specific weight for the
target class; and
determining whether to identify the input text for classification to the target class based
on the composite score and a class-specific decision threshold for the target class.
10. The method as claimed in claim 9, wherein at least one of the first and second scores is
based on a set of one or more noun-words pairs associated with the input text and a set of one or
more noun-word pairs associated with the target class, with at least one noun-word pair in each
set including a noun and a non-adjacent word.
11. The method as claimed in claim 9, wherein determining the first and second scores
comprises determining any two of:
a score based on similarity of at least one or more portions of the input text to text
associated with the target class;
a score based on similarity of a set of one or more non-target classes associated with the
input text and a set of one or more non-target classes associated with the target class;
a score based on probability of the target class given a set of one or more non-target
classes associated with the input text; and
a score based on probability of the target class given at least a portion of the input text.
12. The method as claimed in claim 11, wherein each target class is a document and the text
associated with the target class comprises text of the document or text of another document
associated with the target class.
13. The method as claimed in claim 9,
wherein determining the first and second scores for each target class comprises :
determining the first score based on similarity of at least one or more portions of the input
text to text associated with the target class; and
determining the second score based on similarity of a set of one or more non-target
classes associated with the input text and a set of one or more non-target classes
associated with the target class;
wherein the method comprises determining for each target class:

a third score based on probability of the target class given a set of one or more non-target
classes associated with the input text; and
a fourth score based on probability of the target class given at least a portion of the input
text; and
wherein the composite score is further based on the third score scaled by a third class-
specific weight for the target class and the fourth score scaled by a fourth class-specific weight
for the target class.
14. The method as claimed in claim 9,
wherein the input text is associated with first metadata and each target class is associated
with second metadata; and
wherein at least one of the first and second scores is based on the first metadata and the
second metadata.
15. The method as claimed in claim 14, wherein the first metadata comprises a first set of
non-target classes that are associated with the input text and the second meta data comprises a
second set of non-target classes that are associated with the target class.
16. The method as claimed in claim 2, which involves:
for each target class, determining a composite score based on a first score scaled by a first
class-specific weight for the target class and a second score scaled by a second class-specific
weight for the target class, with the first and second scores based on an input text and text
associated with the target class; and
for each target class, classifying or recommending classification of the input text to the
target class based on the composite score and a class specific decision threshold for the target
class.
17. The method as claimed in claim 16, wherein the first and second scores are selected from
the group consisting of:
a score based on similarity of at least one or more portions of the input text to text
associated with the target class;
a score based on similarity of a set of one or more non-target classes associated with the
input text and a set of one or more non-target classes associated with the target class;
a score based on probability of the target class given a set of one or more non-target classes
associated with the input text; and
a score based on probability of the target class given at least a portion of the input text.
18. The method as claimed in claim 16, which involves:
updating the class-specific threshold for one of the target classes based on acceptance or
rejection of recommended classifications of the input text.

Documents:

742-KOLNP-2004-ABSTRACT 1.1.pdf

742-kolnp-2004-abstract.pdf

742-KOLNP-2004-AMANDED CLAIMS.pdf

742-KOLNP-2004-ASSIGNMENT.pdf

742-kolnp-2004-assignment1.1.pdf

742-kolnp-2004-claims.pdf

742-kolnp-2004-claims1.1.pdf

742-kolnp-2004-correspondence.pdf

742-KOLNP-2004-DESCRIPTION (COMPLETE) 1.1.pdf

742-kolnp-2004-description (complete).pdf

742-KOLNP-2004-DRAWINGS 1.1.pdf

742-kolnp-2004-drawings.pdf

742-kolnp-2004-examination report.pdf

742-KOLNP-2004-FORM 1.1.pdf

742-kolnp-2004-form 1.2.pdf

742-kolnp-2004-form 1.pdf

742-kolnp-2004-form 13.1.pdf

742-KOLNP-2004-FORM 13.pdf

742-kolnp-2004-form 18.1.pdf

742-kolnp-2004-form 18.pdf

742-KOLNP-2004-FORM 2.1.pdf

742-kolnp-2004-form 2.pdf

742-KOLNP-2004-FORM 3.1.pdf

742-kolnp-2004-form 3.2.pdf

742-kolnp-2004-form 3.pdf

742-KOLNP-2004-FORM 5.1.pdf

742-kolnp-2004-form 5.2.pdf

742-kolnp-2004-form 5.pdf

742-kolnp-2004-form 6.1.pdf

742-KOLNP-2004-FORM 6.pdf

742-kolnp-2004-gpa.pdf

742-kolnp-2004-granted-abstract.pdf

742-kolnp-2004-granted-claims.pdf

742-kolnp-2004-granted-description (complete).pdf

742-kolnp-2004-granted-drawings.pdf

742-kolnp-2004-granted-form 1.pdf

742-kolnp-2004-granted-form 2.pdf

742-kolnp-2004-granted-specification.pdf

742-KOLNP-2004-OTHERS 1.1.pdf

742-kolnp-2004-others.pdf

742-KOLNP-2004-PA.pdf

742-KOLNP-2004-REPLY TO EXAMINATION REPORT.pdf

742-kolnp-2004-reply to examination report1.1.pdf

742-kolnp-2004-specification.pdf


Patent Number 246523
Indian Patent Application Number 742/KOLNP/2004
PG Journal Number 09/2011
Publication Date 04-Mar-2011
Grant Date 02-Mar-2011
Date of Filing 02-Jun-2004
Name of Patentee THOMSON REUTERS GLOBAL RESOURCES
Applicant Address LANDIS + GYR-STR.3, ZUG, 6300 SWITZERLAND
Inventors:
# Inventor's Name Inventor's Address
1 AL-KOFAHI KHALID 17 RUNNING BROOK LANE K.A., ROCHESTER, NY 14626
PCT International Classification Number G06F 17/30
PCT International Application Number PCT/US2002/35177
PCT International Filing date 2002-11-01
PCT Conventions:
# PCT Application Number Date of Convention Priority Country
1 60/336,862 2001-11-02 U.S.A.